Ranking of Classification Algorithm in Breast Cancer Based On Estrogen Receptor Using MCDM Technique

Abstract

Classification algorithm selection is an important concern for breast cancer diagnosis. The traditional routine of adopting a unique performance metric for evaluating classifiers is not adequate in the case of micro-array gene expression dataset. This paper introduces an MCDM technique to evaluate classification algorithms in breast cancer forecasting by seeing different performance measure along with feature space. An empirical study is designed to support an overall assessment of classifiers on micro-array datasets using well-known MCDM technique. TOPSIS is used to rank 11 prominent assessment criteria of different classifiers. First, the sequence order of 20 classifiers along with 11 assessment criteria is generated. Further topmost classifiers are grounded on their performances highlighting the role of feature selection in the overall process supporting the genuine assessment of classifiers over any solitary performance criteria. Result indicates that AdaBoostM1 and Iterative Classifier Optimizer are graded as topmost classifiers without and with feature selection, respectively, grounded on their performances on different measures. Furthermore, the proposed MCDM-based model can reconcile distinct or even inconsistent evaluation performance to grasp a group agreement in a complicated decision-making environment.