Selecting features by utilizing intuitionistic fuzzy Entropy method

  • Kiran Pandey Department of Computer Science and Engineering, Technocrats Institute of Technology, Bhopal, Madhya Pradesh, India
  • Arunodaya Mishra Department of Mathematics, Government College Raigaon, Satna, Madhya Pradesh, India
  • Pratibha Rani Department of Engineering Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
  • Jabir Ali School of Engineering and Technology, Department of Computer Science and Engineering, Sharda University, Greater Noida, Uttar Pradesh, India
  • Ripon Chakrabortty Capability Systems Centre, School of Engineering and Information Technology, UNSW Canberra, Australia
Keywords: Intuitionistic fuzzy set, entropy, feature selection, classifier accuracy


Feature selection is the most significant pre-processing activity, which intends to reduce the data dimensionality for enhancing the machine learning process. The evaluation of feature selection must consider classification, performance, efficiency, stability, and many factors. Nowadays, uncertainty is commonly occurred in the feature selection process due to time limitations, imprecise information, and the subjectivity of human minds. Moreover, the theory of intuitionistic fuzzy set has been proven as an extremely valuable tool to tackle the uncertainty and ambiguity that arises in many practical situations. Thus, this study introduces a novel feature selection framework using intuitionistic fuzzy entropy. In this regard, new entropy for IFS is proposed first and then compared with some of the previously developed entropy measures. As entropy is a measure of uncertainty present in data (features), features with higher entropy values are filtered out, and the remaining features having lower entropy values have been used to classify the data. To verify the effectiveness of the proposed entropy-based feature selection, some experiments are done with ten standard benchmark datasets by employing a support vector machine, K-nearest neighbor, and Naïve Bias classifiers. The outcomes of the study validate that the proposed entropy-based filter feature selection is more feasible and impressive than existing filter-based feature selection methods.


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How to Cite
Pandey, K., Mishra, A., Rani, P., Ali, J., & Chakrabortty, R. (2023). Selecting features by utilizing intuitionistic fuzzy Entropy method. Decision Making: Applications in Management and Engineering.
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