@article {Hakkoum2022,
	title = {Interpretability in the medical field: A systematic mapping and review study},
	journal = {Applied Soft Computing},
	volume = {117},
	year = {2022},
	note = {cited By 4},
	abstract = {Context: Recently, the machine learning (ML) field has been rapidly growing, mainly owing to the availability of historical datasets and advanced computational power. This growth is still facing a set of challenges, such as the interpretability of ML models. In particular, in the medical field, interpretability is a real bottleneck to the use of ML by physicians. Therefore, numerous interpretability techniques have been proposed and evaluated to help ML gain the trust of its users. Methods: This review was carried out according to the well-known systematic map and review process to analyze the literature on interpretability techniques when applied in the medical field with regard to different aspects: publication venues and publication year, contribution and empirical types, medical and ML disciplines and objectives, ML black-box techniques interpreted, interpretability techniques investigated, their performance and the best performing techniques, and lastly, the datasets used when evaluating interpretability techniques. Results: A total of 179 articles (1994{\textendash}2020) were selected from six digital libraries: ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, Wiley, and Google Scholar. The results showed that the number of studies dealing with interpretability increased over the years with a dominance of solution proposals and experiment-based empirical type. Diagnosis, oncology, and classification were the most frequent medical task, discipline, and ML objective studied, respectively. Artificial neural networks were the most widely used ML black-box techniques investigated for interpretability. Additionally, global interpretability techniques focusing on a specific black-box model, such as rules, were the dominant explanation types, and most of the metrics used to evaluate interpretability were accuracy, fidelity, and number of rules. Moreover, the variety of the techniques used by the selected papers did not allow categorization at the technique level, and the high number of the sum of evaluations (671) of the articles raised a suspicion of subjectivity. Datasets that contained numerical and categorical attributes were the most frequently used in the selected studies. Conclusions: Further effort is needed in disciplines other than diagnosis and classification. Global techniques such as rules are the most used because of their comprehensibility to doctors, but new local techniques should be explored more in the medical field to gain more insights into the model{\textquoteright}s behavior. More experiments and comparisons against existing techniques are encouraged to determine the best performing techniques. Lastly, quantitative evaluation of interpretability and physicians{\textquoteright} implications in interpretability techniques evaluation is highly recommended to evaluate how the techniques will perform in real-world scenarios. It can ensure the soundness of the techniques and help gain trust in black-box models in medical environments. {\textcopyright} 2022 Elsevier B.V.},
	keywords = {Black box modelling, Black boxes, Computational power, Computer aided diagnosis, Digital libraries, Explainability, Historical dataset, Interpretability, Machine learning, Medical fields, Neural networks, Systematic mapping, Systematic Review, XAI},
	doi = {10.1016/j.asoc.2021.108391},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122624142\&doi=10.1016\%2fj.asoc.2021.108391\&partnerID=40\&md5=38db4d1f5c417a07d0a3204639e157a2},
	author = {Hakkoum, H. and Abnane, I. and Idri, A.}
}
@article {Benaida2022476,
	title = {Machine and~Deep Learning Predictive Techniques for~Blood Glucose Level},
	journal = {Lecture Notes in Networks and Systems},
	volume = {468 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {476-485},
	abstract = {Allowing diabetic patients to predict their BGL is an important task for self-management of their metabolic disease. This allows to avoid hypo or hyperglycaemia by taking appropriate actions. Currently, this is possible due to the development of machine and deep learning techniques which are successfully used in many prediction tasks. This paper evaluates and compares the performances of six ML/DL techniques to forecast BGL predictions; four DL techniques: CNN, LSTM, GRU, DBN and two ML/statistic techniques: SVR, and AR. The evaluation of the performance of the six regressors were in term of four criteria: RMSE, MAE, MMRE, and PRED. In addition, the Scott-Knott were used to evaluate the statistical significance test and to rank the regressors. The results show that AR was the best for 5~min ahead forecasting with a mean of RMSE equal to 8.67~mg/dl. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04826-5_48},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130238876\&doi=10.1007\%2f978-3-031-04826-5_48\&partnerID=40\&md5=8889c0cf2c91459b8f32389a846059f5},
	author = {Benaida, M. and Abnane, I. and Idri, A. and El Idrissi, T.}
}
@article {Ouifak2022456,
	title = {Performance-Interpretability Tradeoff of Mamdani Neuro-Fuzzy Classifiers for Medical Data},
	journal = {Lecture Notes in Networks and Systems},
	volume = {468 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {456-465},
	abstract = {Neuro-fuzzy systems are models that incorporate the learning ability and performance of Artificial Neural Networks (ANNs) with the interpretable reasoning of fuzzy inference systems (FISs). An ANN can learn patterns from data and achieve high accuracy, while a FIS uses linguistic and interpretable rules to match inputs and outputs of the data. Two types of FISs are used the most in literature: Takagi-Sugeno-Kang (TSK) and Mamdani. The main focus of this paper is on the Mamdani neuro-fuzzy systems, notably the Hybrid Neuro-Fuzzy Inference System (HyFIS) and the Neuro-Fuzzy Classifier (NEFCLASS). It aims at evaluating and comparing the two classifiers over two medical datasets to study their performance-interpretability tradeoff. Results show that HyFIS is the best in terms of performance, while NEFCLASS is better in terms of interpretability. As for the performance-interpretability tradeoff, NEFCLASS has the best overall results; it achieves a good performance while being less complicated and more interpretable. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-04826-5_46},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130272925\&doi=10.1007\%2f978-3-031-04826-5_46\&partnerID=40\&md5=65dc74043fe03a59608e34846f4fd7db},
	author = {Ouifak, H. and Idri, A. and Benbriqa, H. and Abnane, I.}
}
@article {Hakkoum2021587,
	title = {Assessing and Comparing Interpretability Techniques for Artificial Neural Networks Breast Cancer Classification},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	volume = {9},
	number = {6},
	year = {2021},
	note = {cited By 11},
	pages = {587-599},
	abstract = {Breast cancer is the most common type of cancer among women. Thankfully, early detection and treatment improvements helped decrease the number of deaths. Data Mining techniques have always assisted BC tasks whether it is screening, diagnosis, prognosis, treatment, monitoring, and/or management. Nowadays, the use of Data Mining is witnessing a new era. In fact, the main objective is no longer to replace humans but to enhance their capabilities, which is why Artificial Intelligence is now referred to as Intelligence Augmentation. In this context, interpretability is used to help domain experts learn new patterns and machine learning experts debug their models. This paper aims to investigate three black-boxes interpretation techniques: Feature Importance, Partial Dependence Plot, and LIME when applied to two types of feed-forward Artificial Neural Networks: Multilayer perceptrons, and Radial Basis Function Network, trained on the Wisconsin Original dataset for breast cancer diagnosis. Results showed that local LIME explanations were instance-level interpretations that came in line with the global interpretations of the other two techniques. Global/local interpretability techniques can thus be combined to define the trustworthiness of a black-box model. {\textcopyright} 2021 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {Article, Artificial intelligence, artificial neural network, Breast Cancer, Breast cancer classifications, cancer diagnosis, Computer aided diagnosis, cross validation, Data mining, Data-mining techniques, Diseases, Domain experts, early diagnosis, entropy, Explainability, Feature importance, Interpretability, Learn+, learning, learning algorithm, Lime, Machine learning, Multilayer neural networks, nerve cell, nonhuman, Partial dependence plot, perceptron, prediction, prognosis, Radial basis function networks, Treatment monitoring},
	doi = {10.1080/21681163.2021.1901784},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103249025\&doi=10.1080\%2f21681163.2021.1901784\&partnerID=40\&md5=78e1e57a62692bab2b39984182af7904},
	author = {Hakkoum, H. and Idri, A. and Abnane, I.}
}
@article {Benbriqa2021588,
	title = {Deep and Ensemble Learning Based Land Use and Land Cover Classification},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12951 LNCS},
	year = {2021},
	note = {cited By 1},
	pages = {588-604},
	abstract = {Monitoring of Land use and Land cover (LULC) changes is a highly encumbering task for humans. Therefore, machine learning based classification systems can help to deal with this challenge. In this context, this study evaluates and compares the performance of two Single Learning (SL) techniques and one Ensemble Learning (EL) technique. All the empirical evaluations were over the open source LULC dataset proposed by the German Center for Artificial Intelligence (EuroSAT), and used the performance criteria -accuracy, precision, recall, F1 score and change in accuracy for the EL classifiers-. We firstly evaluate the performance of SL techniques: Building and optimizing a Convolutional Neural Network architecture, implementing Transfer learning, and training Machine learning algorithms on visual features extracted by Deep Feature Extractors. Second, we assess EL techniques and compare them with SL classifiers. Finally, we compare the capability of EL and hyperparameter tuning to improve the performance of the Deep Learning models we built. These experiments showed that Transfer learning is the SL technique that achieves the highest accuracy and that EL can indeed outperform the SL classifiers. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Classification (of information), Convolutional neural networks, Deep feature extraction, Deep learning, Ensemble learning, Features extraction, Hyper-parameter optimizations, Land cover, Land use, Learning algorithms, Learning classifiers, Learning techniques, Network architecture, Performance, Transfer learning},
	doi = {10.1007/978-3-030-86970-0_41},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115689890\&doi=10.1007\%2f978-3-030-86970-0_41\&partnerID=40\&md5=910871e0f58b4f00460e5e2509929a23},
	author = {Benbriqa, H. and Abnane, I. and Idri, A. and Tabiti, K.}
}
@article {CHLIOUI20211039,
	title = {Ensemble case based reasoning imputation in breast cancer classification},
	journal = {Journal of Information Science and Engineering},
	volume = {37},
	number = {5},
	year = {2021},
	note = {cited By 1},
	pages = {1039-1051},
	abstract = {Missing Data (MD) is a common drawback that affects breast cancer classification. Thus, handling missing data is primordial before building any breast cancer classifier. This paper presents the impact of using ensemble Case-Based Reasoning (CBR) imputation on breast cancer classification. Thereafter, we evaluated the influence of CBR using parameter tuning and ensemble CBR (E-CBR) with three missingness mechanisms (MCAR: Missing completely at random, MAR: Missing at random and NMAR: not missing at random) and nine percentages (10\% to 90\%) on the accuracy rates of five classifiers: Decision trees, Random forest, K-nearest neighbor, Support vector machine and Multi-layer perceptron over two Wisconsin breast cancer datasets. All experiments were implemented using Weka JAVA API code 3.8; SPSS v20 was used for statistical tests. The findings confirmed that E-CBR yields to better results compared to CBR for the five classifiers. The MD percentage affects negatively the classifier performance: As the MD percentage increases, the accuracy rates of the classifier decrease regardless the MD mechanism and technique. RF with E-CBR outperformed all the other combinations (MD technique, classifier) with 89.72\% for MCAR, 87.08\% for MAR and 86.84\% for NMAR. {\textcopyright} 2021 Institute of Information Science. All rights reserved.},
	keywords = {Accuracy rate, Breast Cancer, Breast cancer classifications, Cancer classifier, Case based reasoning, Case-based reasoning imputation, Casebased reasonings (CBR), Classification (of information), Data handling, Decision trees, Diseases, Ensemble, Missing at randoms, Missing data, Nearest neighbor search, Parameters tuning, Support vector machines},
	doi = {10.6688/JISE.202109_37(5).0004},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115966179\&doi=10.6688\%2fJISE.202109_37\%285\%29.0004\&partnerID=40\&md5=97c15046a8900f9df38ec3430801c844},
	author = {Chlioui, I. and Idri, A. and Abnane, I. and EZZAT, M.}
}
@conference {Abnane20211,
	title = {Heterogeneous ensemble imputation for software development effort estimation},
	booktitle = {PROMISE 2021 - Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering, co-located with ESEC/FSE 2021},
	year = {2021},
	note = {cited By 1},
	pages = {1-10},
	abstract = {Choosing the appropriate Missing Data (MD) imputation technique for a given Software development effort estimation (SDEE) technique is not a trivial task. In fact, the impact of the MD imputation on the estimation output depends on the dataset and the SDEE technique used and there is no best imputation technique in all contexts. Thus, an attractive solution is to use more than one single imputation technique and combine their results for a final imputation outcome. This concept is called ensemble imputation and can help to significantly improve the estimation accuracy. This paper develops and evaluates a heterogeneous ensemble imputation whose members were the four single imputation techniques: K-Nearest Neighbors (KNN), Expectation Maximization (EM), Support Vector Regression (SVR), and Decision Trees (DT). The impact of the ensemble imputation was evaluated and compared with those of the four single imputation techniques on the accuracy measured in terms of the standardized accuracy criterion of four SDEE techniques: Case Based Reasoning (CBR), Multi-Layers Perceptron (MLP), Support Vector Regression (SVR) and Reduced Error Pruning Tree (REPTree). The Wilcoxon statistical test was also performed in order to assess whether the results are significant. All the empirical evaluations were carried out over the six datasets, namely, ISBSG, China, COCOMO81, Desharnais, Kemerer, and Miyazaki. Results show that the use of heterogeneous ensemble-based imputation instead single imputation significantly improved the accuracy of the four SDEE techniques. Indeed, the ensemble imputation technique was ranked either first or second in all contexts. {\textcopyright} 2021 ACM.},
	keywords = {Case based reasoning, Casebased reasonings (CBR), Decision trees, Empirical evaluations, Expectation Maximization, Forestry, Heterogeneous ensembles, Imputation techniques, K nearest neighbor (KNN), Maximum principle, Multilayer neural networks, Nearest neighbor search, Predictive analytics, Software design, Software development effort, Support vector regression, Support vector regression (SVR)},
	doi = {10.1145/3475960.3475984},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113586361\&doi=10.1145\%2f3475960.3475984\&partnerID=40\&md5=7bd6bbfb801a84cd7694e3713d2d0081},
	author = {Abnane, I. and Idri, A. and Hosni, M. and Abran, A.}
}
@article {Hosni20212827,
	title = {A systematic mapping study for ensemble classification methods in cardiovascular disease},
	journal = {Artificial Intelligence Review},
	volume = {54},
	number = {4},
	year = {2021},
	note = {cited By 5},
	pages = {2827-2861},
	abstract = {Ensemble methods overcome the limitations of single machine learning techniques by combining different techniques, and are employed in the quest to achieve a high level of accuracy. This approach has been investigated in various fields, one of them being that of bioinformatics. One of the most frequent applications of ensemble techniques involves research into cardiovascular diseases, which are considered the leading cause of death worldwide. The purpose of this research work is to identify the papers that investigate ensemble classification techniques applied to cardiology diseases, and to analyse them according to nine aspects: their publication venues, the medical tasks tackled, the empirical and research types adopted, the types of ensembles proposed, the single techniques used to construct the ensembles, the validation frameworks adopted to evaluate the proposed ensembles, the tools used to build the ensembles, and the optimization methods employed for the single techniques. This paper reports the carrying out of a systematic mapping study. An extensive automatic search in four digital libraries: IEEE Xplore, ACM Digital Library, PubMed, and Scopus, followed by a study selection process, resulted in the identification of 351 papers that were used to address our mapping questions. This study found that the papers selected had been published in a large number of different resources. The medical task addressed most frequently by the selected studies was diagnosis. In addition, the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted by the selected studies. Homogeneous ensembles were the ensemble type that was developed most often in literature, while decision trees, artificial neural networks and Bayesian classifiers were the single techniques used most frequently to develop ensemble classification methods. The weighted majority and majority voting rules were adopted to obtain the final decision of the ensembles developed. With regard to evaluation frameworks, the datasets obtained from the UCI and PhysioBank repositories were those used most often to evaluate the ensemble methods, while the k-fold cross-validation method was the most frequently-employed validation technique. Several tools with which to build ensemble classifiers were identified, and the type of software adopted with the greatest frequency was open source. Finally, only a few researchers took into account the optimization of the parameter settings of either single or meta ensemble classifiers. This mapping study attempts to provide a greater insight into the application of ensemble classification methods in cardiovascular diseases. The majority of the selected papers reported positive feedback as regards the ability of ensemble methods to perform better than single methods. Further analysis is required to aggregate the evidence reported in literature. {\textcopyright} 2020, Springer Nature B.V.},
	keywords = {Bayesian networks, Cardio-vascular disease, Cardiology, Decision trees, Diagnosis, Digital libraries, Diseases, Ensemble classification, Ensemble classifiers, Evaluation framework, K fold cross validations, Learning systems, Majority voting rules, Mapping, Open source software, Open systems, Optimization method, Systematic mapping studies},
	doi = {10.1007/s10462-020-09914-6},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091735819\&doi=10.1007\%2fs10462-020-09914-6\&partnerID=40\&md5=69ea4b02de420c3ec6a85e1f3c7dddaf},
	author = {Hosni, M. and Carrillo de Gea, J.M. and Idri, A. and El Bajta, M. and Fern{\'a}ndez Alem{\'a}n, J.L. and Garc{\'\i}a-Mateos, G. and Abnane, I.}
}
@article {Hakkoum202015,
	title = {Artificial Neural Networks Interpretation Using LIME for Breast Cancer Diagnosis},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1161 AISC},
	year = {2020},
	note = {cited By 6},
	pages = {15-24},
	abstract = {Breast Cancer (BC) is the most common type of cancer among women. Thankfully early detection and treatment improvements helped decrease its number of deaths. Data Mining techniques (DM), which discover hidden and potentially useful patterns from data, particularly for breast cancer diagnosis, are witnessing a new era, where the main objective is no longer replacing humans or just assisting them in their tasks but enhancing and augmenting their capabilities and this is where interpretability comes into play. This paper aims to investigate the Local Interpretable Model-agnostic Explanations (LIME) technique to interpret a Multilayer perceptron (MLP) trained on the Wisconsin Original Data-set. The results show that LIME explanations are a sort of real-time interpretation that helps understanding how the constructed neural network {\textquotedblleft}thinks{\textquotedblright} and thus can increase trust and help oncologists, as the domain experts, learn new patterns. {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Breast Cancer, Breast cancer diagnosis, Data mining, Diseases, Domain experts, Information systems, Information use, Interpretability, Lime, Multi layer perceptron, Neural networks, Real time, Useful patterns, WISCONSIN},
	doi = {10.1007/978-3-030-45697-9_2},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085506923\&doi=10.1007\%2f978-3-030-45697-9_2\&partnerID=40\&md5=cf809f0c6b9c95cd37d48038f1dfa79a},
	author = {Hakkoum, H. and Idri, A. and Abnane, I.}
}
@article {Idri20201239,
	title = {Assessing the impact of parameters tuning in ensemble based breast Cancer classification},
	journal = {Health and Technology},
	volume = {10},
	number = {5},
	year = {2020},
	note = {cited By 12},
	pages = {1239-1255},
	abstract = {Breast cancer is one of the major causes of death among women. Different decision support systems were proposed to assist oncologists to accurately diagnose their patients. These decision support systems mainly used classification techniques to categorize the diagnosis into Malign or Benign tumors. Given that no consensus has been reached on the classifier that can perform best in all circumstances, ensemble-based classification, which classifies patients by combining more than one single classification technique, has recently been investigated. In this paper, heterogeneous ensembles based on three well-known machine learning techniques (support vector machines, multilayer perceptron, and decision trees) were developed and evaluated by investigating the impact of parameter values of the ensemble members on classification performance. In particular, we investigate three parameters tuning techniques: Grid Search (GS), Particle Swarm Optimization (PSO) and the default parameters of the Weka Tool to evaluate whether setting ensemble parameters permits more accurate classification in breast cancer over four datasets obtained from the Machine Learning repository. The heterogeneous ensembles of this study were built using the majority voting technique as a combination rule. The overall results obtained suggest that: (1) Using GS or PSO techniques for single techniques provide more accurate classification; (2) In general, ensembles generate more accurate classification than their single techniques regardless of the optimization techniques used. (3) Heterogeneous ensembles based on optimized single classifiers generate better results than the Uniform Configuration of Weka (UC-WEKA) ensembles, and (4) PSO and GS slightly have the same impact on the performances of ensembles. {\textcopyright} 2020, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.},
	keywords = {accuracy, Article, Breast Cancer, cancer classification, classifier, decision tree, experimental design, grid search, human, multilayer perceptron, particle swarm optimization, recall, support vector machine},
	doi = {10.1007/s12553-020-00453-2},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087361107\&doi=10.1007\%2fs12553-020-00453-2\&partnerID=40\&md5=7398903f3007d71e535b12c2ef9a90a6},
	author = {Idri, A. and Bouchra, E.O. and Hosni, M. and Abnane, I.}
}
@article {Chlioui202061,
	title = {Comparing Statistical and Machine Learning Imputation Techniques in Breast Cancer Classification},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12252 LNCS},
	year = {2020},
	note = {cited By 1},
	pages = {61-76},
	abstract = {Missing data imputation is an important task when dealing with crucial data that cannot be discarded such as medical data. This study evaluates and compares the impacts of two statistical and two machine learning imputation techniques when classifying breast cancer patients, using several evaluation metrics. Mean, Expectation-Maximization (EM), Support Vector Regression (SVR) and K-Nearest Neighbor (KNN) were applied to impute 18\% of missing data missed completely at random in the two Wisconsin datasets. Thereafter, we empirically evaluated these four imputation techniques when using five classifiers: decision tree (C4.5), Case Based Reasoning (CBR), Random Forest (RF), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). In total, 1380 experiments were conducted and the findings confirmed that classification using imputation based machine learning outperformed classification using statistical imputation. Moreover, our experiment showed that SVR was the best imputation method for breast cancer classification. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Breast cancer classifications, Case based reasoning, Casebased reasonings (CBR), Decision trees, Diseases, Expectation Maximization, Imputation techniques, K nearest neighbor (KNN), Learning systems, Maximum principle, Missing data imputations, Multi layer perceptron, Multilayer neural networks, Nearest neighbor search, Support vector machines, Support vector regression, Support vector regression (SVR)},
	doi = {10.1007/978-3-030-58811-3_5},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092259110\&doi=10.1007\%2f978-3-030-58811-3_5\&partnerID=40\&md5=fd5730e8014d40306df07238106d32ff},
	author = {Chlioui, I. and Abnane, I. and Idri, A.}
}
@article {Chlioui2020547,
	title = {Data preprocessing in knowledge discovery in breast cancer: systematic mapping study},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization},
	volume = {8},
	number = {5},
	year = {2020},
	note = {cited By 5},
	pages = {547-561},
	abstract = {Data Mining (DM) is a set of techniques that allow to analyse data from different perspectives and summarising it into useful information. Data mining has been increasingly used in medicine, especially in oncology. Data preprocessing is the most important step of knowledge extraction process and allows to improve the performance of the DM models. Breast cancer (BC) becomes the most common cancer among females worldwide and the leading cause of women{\textquoteright}s death. This paper aims to perform a systematic mapping study to analyse and synthesise studies on the application of preprocessing techniques for a DM task in breast cancer.Therefore, 66 relevant articles published between 2000 and October 2018 were selected and analysed according to five criteria: year/channel of publication, research type, medical task, empirical type and preprocessing task. The results show that Conferences and journals are the most targeted publication sources, researchers were more interested in applying preprocessing techniques for the diagnosis of BC, historical-based evaluation was the most used empirical type in the evaluation of preprocessing techniques in BC, and data reduction was the most investigated task of preprocessing in BC. However, A low number of papers discussed treatment which encourages researchers to devote more efforts to this task. {\textcopyright} 2020 Informa UK Limited, trading as Taylor \& Francis Group.},
	keywords = {algorithm, Article, Breast Cancer, cancer classification, cancer prognosis, clinical assessment, clinical outcome, Data mining, Data mining models, Data mining tasks, Data preprocessing, Diagnosis, diagnostic accuracy, Diseases, Extraction process, health promotion, human, image analysis, knowledge, knowledge discovery, Knowledge extraction, Machine learning, Mapping, Medical informatics, nerve cell network, neural crest cell, Performance, Pre-processing techniques, processing, screening test, Systematic mapping studies, Systematic Review, validity},
	doi = {10.1080/21681163.2020.1730974},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080112312\&doi=10.1080\%2f21681163.2020.1730974\&partnerID=40\&md5=befb1bc3f31f676a8e95bbc5bff5ab6d},
	author = {Chlioui, I. and Idri, A. and Abnane, I.}
}
@article {Abnane2020,
	title = {Fuzzy case-based-reasoning-based imputation for incomplete data in software engineering repositories},
	journal = {Journal of Software: Evolution and Process},
	volume = {32},
	number = {9},
	year = {2020},
	note = {cited By 6},
	abstract = {Missing data is a serious issue in software engineering because it can lead to information loss and bias in data analysis. Several imputation techniques have been proposed to deal with both numerical and categorical missing data. However, most of those techniques used is simple reuse techniques originally designed for numerical data, which is a problem when the missing data are related to categorical attributes. This paper aims (a) to propose a new fuzzy case-based reasoning (CBR) imputation technique designed for both numerical and categorical data and (b) to evaluate and compare the performance of the proposed technique with the k-nearest neighbor (KNN) imputation technique in terms of error and accuracy under different missing data percentages and missingness mechanisms in four software engineering data sets. The results suggest that the proposed fuzzy CBR technique outperformed KNN in terms of imputation error and accuracy regardless of the missing data percentage, missingness mechanism, and data set used. Moreover, we found that the missingness mechanism has an important impact on the performance of both techniques. The results are encouraging in the sense that using an imputation technique designed for both categorical and numerical data is better than reusing methods originally designed for numerical data. {\textcopyright} 2020 John Wiley \& Sons, Ltd.},
	keywords = {accuracy, Case based reasoning, Categorical data, Clustering algorithms, Empirical Software Engineering, Fuzzy analogy, imputation, Missing data, Nearest neighbor search, Numerical methods, Software engineering},
	doi = {10.1002/smr.2260},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081904769\&doi=10.1002\%2fsmr.2260\&partnerID=40\&md5=762cb4270c6a55d209feaa8eb6df5c5f},
	author = {Abnane, I. and Idri, A. and Abran, A.}
}
@article {Idri2018,
	title = {Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation},
	journal = {Journal of Software: Evolution and Process},
	volume = {30},
	number = {4},
	year = {2018},
	doi = {10.1002/smr.1925},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045686530\&doi=10.1002\%2fsmr.1925\&partnerID=40\&md5=3a3d72a1a9b32d7a365db2168b5e3a45},
	author = {Idri, A. and Abnane, I. and Abran, A.}
}
@conference {Abnane20181015,
	title = {Improved analogy-based effort estimation with incomplete mixed data},
	booktitle = {Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018},
	year = {2018},
	pages = {1015-1024},
	doi = {10.15439/2018F95},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057257109\&doi=10.15439\%2f2018F95\&partnerID=40\&md5=04ce7873db15a5eed3fd50a320b73e4f},
	author = {Abnane, I. and Idri, A.}
}
@article {Idri2018,
	title = {Support vector regression-based imputation in analogy-based software development effort estimation},
	journal = {Journal of Software: Evolution and Process},
	volume = {30},
	number = {12},
	year = {2018},
	doi = {10.1002/smr.2114},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058493764\&doi=10.1002\%2fsmr.2114\&partnerID=40\&md5=09059bd4e9087fda5b7225f1c8aeea9d},
	author = {Idri, A. and Abnane, I. and Abran, A.}
}
@conference {Abnane20171302,
	title = {Empirical evaluation of fuzzy analogy for Software Development Effort Estimation},
	booktitle = {Proceedings of the ACM Symposium on Applied Computing},
	volume = {Part F128005},
	year = {2017},
	note = {cited By 0},
	pages = {1302-1304},
	abstract = {Software Development Effort Estimation (SDEE) plays a primary role in software project management. Among several techniques suggested for estimating software development effort, analogybased software effort estimation approaches stand out as promising techniques. In this paper, the performance of Fuzzy Analogy is compared with that of six other SDEE techniques (Linear Regression, Support Vector Regression, Multi-Layer Perceptron, M5P and Classical Analogy). The first step of the evaluation aimed to ensure that the SDEE techniques outperformed random guessing by using the Standardized Accuracy (SA). Then, we used a set of reliable performance measures and Borda count to rank them and identify which techniques are the most accurate. The results suggest that Fuzzy Analogy statistically outperformed the other SDEE techniques regardless of the dataset used. {\textcopyright} 2017 ACM.},
	doi = {10.1145/3019612.3019905},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020898807\&doi=10.1145\%2f3019612.3019905\&partnerID=40\&md5=df809b8bee59594505af4961af714d5e},
	author = {Abnane, I. and Idri, A. and Abran, A.}
}
@conference {Abnane2017,
	title = {Evaluating Fuzzy Analogy on incomplete software projects data},
	booktitle = {2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016},
	year = {2017},
	note = {cited By 0},
	abstract = {Missing Data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort prediction systems. This paper investigates the use of missing data (MD) techniques with Fuzzy Analogy. More specifically, this study analyze the predictive performance of this analogy-based technique when using toleration, deletion or k-nearest neighbors (KNN) imputation techniques using the Pred(0.25) accuracy criterion and thereafter compares the results with the findings when using the Standardized Accuracy (SA) measure. A total of 756 experiments were conducted involving seven data sets, three MD techniques (toleration, deletion and KNN imputation), three missingness mechanisms (MCAR: missing completely at random, MAR: missing at random, NIM: non-ignorable missing), and MD percentages from 10 percent to 90 percent. The results of accuracy measured in terms of Pred(0.25) confirm the findings of a study which used the SA measure. Moreover, we found that SA and Pred(0.25) measure different aspects of technique performance. Hence, SA is not sufficient to conclude about the technique accuracy and it should be used with other metrics, especially Pred(0.25). {\textcopyright} 2016 IEEE.},
	doi = {10.1109/SSCI.2016.7849922},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016044171\&doi=10.1109\%2fSSCI.2016.7849922\&partnerID=40\&md5=9ece9d331d7bf0bbef07ed39c97ca2d1},
	author = {Abnane, I. and Idri, A.}
}
@conference {Idri2017114,
	title = {Fuzzy Analogy Based Effort Estimation: An Empirical Comparative Study},
	booktitle = {IEEE CIT 2017 - 17th IEEE International Conference on Computer and Information Technology},
	year = {2017},
	note = {cited By 0},
	pages = {114-121},
	abstract = {Software Development Effort Estimation (SDEE) plays a primary role in software project management. Among several techniques suggested for estimating software development effort, analogy-based software effort estimation approaches stand out as promising techniques.In this paper, the performance of Fuzzy Analogy is compared with that of six other SDEE techniques (Linear Regression, Support Vector Regression, Multi-Layer Perceptron, M5P and Classical Analogy). The evaluation of the SDEE techniques was performed over seven datasets with two evaluation techniques (All-in and Jackknife). The first step of the evaluation aimed to ensure that the SDEE techniques outperformed random guessing by using the Standardized Accuracy (SA). Then, we used a set of reliable performance measures (Pred(0.25), MAE, MBRE, MIBRE and LSD) and Borda count to rank them and identify which techniques are the most accurate.The results suggest that when using All-in evaluation, Fuzzy Analogy statistically outperformed the other SDEE techniques regardless of the dataset used. However, when using Jackknife evaluation, the results obtained depended on the dataset and the SDEE technique used. The results suggest that Fuzzy Analogy is a promising technique for software development effort estimation. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/CIT.2017.29},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032383384\&doi=10.1109\%2fCIT.2017.29\&partnerID=40\&md5=2d4f527b6750abd19f4a3787348a493a},
	author = {Idri, A. and Abnane, I.}
}
