@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.}
}
@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 {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.}
}
