Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators

• Published in: IEEE access Vol. 10; pp. 67771 - 67786
• Main Authors: Kavitha, S., Janani, K., Kumar, J. Satheesh, Elkhouly, Mahmoud M., Amudha, T.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agglomeration; Aggregation operators; Algorithms; Arithmetic; Correlation; Data integration; Decision making; dual hesitant q-rung orthopair fuzzy sets; Feature extraction; Feature selection; Filtering algorithms; Fuzzy sets; machine learning; Mathematical analysis; multi label feature selection; Operators (mathematics); Rough sets; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3185765

In this article, the feature selection (FS) process is taken as a multi criteria decision making (MCDM) problem. Also, to consider the impreciseness arising in the real time data, the values of the decision matrix procured after the ridge regression is fuzzified into dual hesitant q-rung orthopair fuzzy set. For the information fusion process, we have proposed various aggregation operators such as the Dual Hesitant q-rung orthopair fuzzy weighted Dombi arithmetic aggregation operator, Dual Hesitant q-rung orthopair fuzzy weighted Dombi geometric aggregation operator, Dual Hesitant q-rung orthopair fuzzy ordered weighted Dombi arithmetic aggregation operator and Dual Hesitant q-rung orthopair fuzzy ordered weighted Dombi geometric aggregation operator. A multi-label feature selection method is proposed using these MCDM techniques formed by the aggregation operators. This algorithm, initially, obtains the values of the decision matrix through the process of ridge regression. The weight vector required for the MCDM process is calculated using entropy. Further, the data are fuzzified and the MCDM process proposed using the aforementioned aggregation operators are utilized. A rank vector is obtained by utilizing the score function to select the desired number of features. It should be noted that through changing the aggregation operator, the algorithm can be altered. Experimental evaluation that compares the proposed method to other existing methods in terms of evaluation metrics demonstrates the effectiveness of the proposed method and their significance is also evaluated.