Abstract
Missing Data (MD) is a common drawback when applying Data Mining on breast cancer datasets since it affects the ability of the Data mining classifier. This study evaluates the influence of MD on three classifiers: Decision tree C4.5, Support vector machine (SVM), and Multi-Layer Perceptron (MLP). For this purpose, 162 experiments were conducted using KNN imputation with three missingness mechanisms (MCAR, MAR and NMAR), and nine percentages (form 10% to 90%) applied on two Wisconsin breast cancer datasets. The MD percentage affects negatively the classifier performance. MLP achieved the lowest accuracy rates regardless the MD mechanism/percentage.
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Chlioui, I., Idri, A., Abnane, I., de Gea, J.M.C., Fernández-Alemán, J.L. (2019). Breast Cancer Classification with Missing Data Imputation. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 932. Springer, Cham. https://doi.org/10.1007/978-3-030-16187-3_2
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