@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.}
}
@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 {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.}
}
