A MCDM-based performance of classification algorithms in breast cancer prediction for imbalanced datasets. (2nd February 2022)

• Record Type: Journal Article
• Authors: Lamba, Monika; Munjal, Geetika; Gigras, Yogita
• Is Part Of: International journal of intelligent engineering informatics. Volume 9:Number 5(2021)
• Page Start: 425
• Page End: 454
• Publication Date: 2022-02-02
• Subjects: MCDM -- multi-criteria decision making -- VIKOR -- VlseKriterijumska optimizacija I Komprominso resenje -- TOPSIS -- technique for order preference by similarity to ideal solution -- GRC -- grey relational coefficient -- breast cancer -- SRCC -- Spearman's rank correlation coefficient; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

The choice of a suitable classification algorithm is extremely important to worry about in different areas involving cancer diagnosis. It associates more than one benchmark for selecting the correct classification algorithm, so the appropriate choice called for multiple criteria decision making (MCDM). Several techniques of MCDM adjudicator may help in ranking of numerous classifiers. The main motive is to propose a two-step approach for determining conclusive ranking of classification algorithms utilising three MCDM techniques and Spearman's rank correlation coefficient (SRCC). Twenty classification algorithms are ranked utilising 12 performance benchmarks on seven breast cancer microarray datasets. Primary ranking generated on classifier's results using technique for order preference by similarity to ideal solution (TOPSIS), VlseKriterijumska optimizacija I Komprominso resenje (VIKOR) and grey relational coefficient (GRC) are nonconclusive. Thus, spearman's rank correlation coefficient is used to settle the differences in primary ranking and produce secondary ranking which resulted in identifying common top classifiers among seven micro-array gene expression datasets.