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This proposed method is based on dynamic mutual information, which can handle redundancy among features controlling the input space. We compare the proposed method with some existing problem transformation and algorithm adaptation methods applied to real multi-label datasets using the metrics of multi-label accuracy and hamming loss. The results show that the proposed method demonstrates more stable and better performance for nearly all multi-label datasets.<\/jats:p>","DOI":"10.3233\/ida-226666","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T13:03:10Z","timestamp":1685710990000},"page":"891-909","source":"Crossref","is-referenced-by-count":4,"title":["Dynamic mutual information-based feature selection for multi-label learning"],"prefix":"10.1177","volume":"27","author":[{"given":"Kyung-Jun","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-Hyuck","family":"Jun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-226666_ref3","doi-asserted-by":"crossref","unstructured":"G. Doquire and M. 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