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A Novel Intuitionistic Fuzzy Inference System for Feature Subset Selection in Weather Prediction

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Abstract

This work presents a novel approach for optimal feature subset (OFS) selection in weather prediction (WP), addressing the challenge of handling a large number of features. The proposed method is a filter-based technique utilizing an intuitionistic fuzzy inference system (IFIS) designed to assess relationships between meteorological features while incorporating geographical factors. The core focus is on the utilisation of the 'hesitation degree' (HD) as a measure of feature importance, a concept applied for the first time in this domain. The method is compared against traditional and state-of-the-art algorithms, including custom fuzzy inference systems (FIS) and several variations of IFIS, showcasing its superiority in terms of accuracy (ACC), precision (PRE), recall (REC), and f1-score (F1S) across various classifiers. The computational analysis affirms the simplicity and efficiency of the proposed method. The main contributions encompass the development of a computationally efficient filter-based feature selection (FS) method, the integration of geographical features, and the emphasis on the HD for a nuanced FS, demonstrating robust performance in scenarios involving nonlinear relationships between features and the target feature.

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Authors and Affiliations

  1. Indira Gandhi Delhi Technical University for Women, Delhi, 110006, India

    Kavya Gupta & Devendra Kumar Tayal

  2. Netaji Subhas University of Technology, Delhi, 110031, India

    Aarti Jain

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  1. Kavya Gupta
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  2. Devendra Kumar Tayal
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Correspondence to Kavya Gupta.

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Gupta, K., Tayal, D.K. & Jain, A. A Novel Intuitionistic Fuzzy Inference System for Feature Subset Selection in Weather Prediction. Wireless Pers Commun 133, 831–849 (2023). https://doi.org/10.1007/s11277-023-10793-7

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