@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.}
}
