Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators
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 f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9804723/ |
| _version_ | 1811316663062102016 |
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| author | S. Kavitha K. Janani J. Satheesh Kumar Mahmoud M. Elkhouly T. Amudha |
| author_facet | S. Kavitha K. Janani J. Satheesh Kumar Mahmoud M. Elkhouly T. Amudha |
| author_sort | S. Kavitha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T11:53:22Z |
| format | Article |
| id | doaj.art-fedf22b994354bb09c36ffe4a8173b77 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T11:53:22Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fedf22b994354bb09c36ffe4a8173b772022-12-22T02:47:59ZengIEEEIEEE Access2169-35362022-01-0110677716778610.1109/ACCESS.2022.31857659804723Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation OperatorsS. Kavitha0https://orcid.org/0000-0002-5400-1653K. Janani1https://orcid.org/0000-0002-8701-6915J. Satheesh Kumar2Mahmoud M. Elkhouly3https://orcid.org/0000-0002-9806-5800T. Amudha4Department of Computer Applications, Bharathiar University, Coimbatore, IndiaDepartment of Mathematics, Bharathiar University, Coimbatore, IndiaDepartment of Computer Applications, Bharathiar University, Coimbatore, IndiaDepartment of Information Technology, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, EgyptDepartment of Computer Applications, Bharathiar University, Coimbatore, IndiaIn 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.https://ieeexplore.ieee.org/document/9804723/Aggregation operatorsdecision makingdual hesitant q-rung orthopair fuzzy setsmachine learningmulti label feature selection |
| spellingShingle | S. Kavitha K. Janani J. Satheesh Kumar Mahmoud M. Elkhouly T. Amudha Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators IEEE Access Aggregation operators decision making dual hesitant q-rung orthopair fuzzy sets machine learning multi label feature selection |
| title | Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators |
| title_full | Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators |
| title_fullStr | Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators |
| title_full_unstemmed | Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators |
| title_short | Multi Label Feature Selection Through Dual Hesitant q-Rung Orthopair Fuzzy Dombi Aggregation Operators |
| title_sort | multi label feature selection through dual hesitant q rung orthopair fuzzy dombi aggregation operators |
| topic | Aggregation operators decision making dual hesitant q-rung orthopair fuzzy sets machine learning multi label feature selection |
| url | https://ieeexplore.ieee.org/document/9804723/ |
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