Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods

By Gang Kou, Pei Yang, Yi Peng, Feng Xiao, Yang Chen, F. Alsaadi. Applied Soft Computing, 2020

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Abstract The evaluation of feature selection methods for text classification with small sample datasets must consider classification performance, stability, and efficiency. It is, thus, a multiple criteria decision-making (MCDM) problem. Yet there has been few research in feature selection evaluation using MCDM methods which considering multiple criteria. Therefore, we use MCDM-based methods for evaluating feature selection methods for text classification with small sample datasets. An experimental study is designed to compare five MCDM methods to validate the proposed approach with 10 feature selection methods, nine evaluation measures for binary classification, seven evaluation measures for multi-class classification, and three classifiers with 10 small datasets. Based on the ranked results of the five MCDM methods, we make recommendations concerning feature selection methods. The results demonstrate the effectiveness of the used MCDM-based method in evaluating feature selection methods.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/14b0511c098b0bc739ffea8e848175b46e55464a