Titlebook: How Fuzzy Concepts Contribute to Machine Learning; Mahdi Eftekhari,Adel Mehrpooya,Vicen? Torra Book 2022 The Editor(s) (if applicable) and

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Unsupervised Feature Selection Method Based on Sensitivity and Correlation Concepts for Multiclass Pive clustering and the concepts of sensitivity and Pearson’s correlation. We show how this method is employed as the fitness function in a genetic algorithm (GA) in order to evaluate feature subsets. Informally, the method works as follows. First, the sensitivity index of each feature is computed by

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Hesitant Fuzzy Decision Tree Approach for Highly Imbalanced Data Classificationd data when the distribution of data samples is not the same in different classes. That is, there is usually a large difference among the number of instances in different classes. If this is the case, learning algorithms, with their goal of maximizing the accuracy of the inferred model, may ignore t

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Ensemble of Feature Selection Methods: A Hesitant Fuzzy Set Based Approach significantly smaller than the number of instances, this is not the case for DNA microarray data. This chapter introduces a feature selection algorithm based on a greedy search, and it uses main concepts from hesitant fuzzy set theory as an heuristic to tackle the feature selection problem for high

harbinger

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A Hybrid Filter-Based Feature Selection Method via Hesitant Fuzzy and Rough Sets Conceptsthe significant features. In particular, the approach described in this chapter is based on the combination of concepts related to rough set theory to build a feature selection algorithm. The concepts considered include weighted rough sets, fuzzy rough sets, and hesitant fuzzy sets.