{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T14:33:48Z","timestamp":1785335628108,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nature Science Foundation of China","award":["62376114"],"award-info":[{"award-number":["62376114"]}]},{"name":"Nature Science Foundation of China","award":["62372488"],"award-info":[{"award-number":["62372488"]}]},{"name":"Nature Science Foundation of China","award":["MSGJB2021007"],"award-info":[{"award-number":["MSGJB2021007"]}]},{"name":"Nature Science Foundation of China","award":["ZZ2024J20"],"award-info":[{"award-number":["ZZ2024J20"]}]},{"name":"Nature Science Foundation of China","award":["FBJY20230170"],"award-info":[{"award-number":["FBJY20230170"]}]},{"name":"Nature Science Foundation of China","award":["2020CGZH0009"],"award-info":[{"award-number":["2020CGZH0009"]}]},{"name":"Nature Science Foundation of China","award":["XQZX20240032"],"award-info":[{"award-number":["XQZX20240032"]}]},{"name":"High-level cultivation projects of Minnan Normal University","award":["62376114"],"award-info":[{"award-number":["62376114"]}]},{"name":"High-level cultivation projects of Minnan Normal University","award":["62372488"],"award-info":[{"award-number":["62372488"]}]},{"name":"High-level cultivation projects of Minnan Normal University","award":["MSGJB2021007"],"award-info":[{"award-number":["MSGJB2021007"]}]},{"name":"High-level cultivation projects of Minnan Normal University","award":["ZZ2024J20"],"award-info":[{"award-number":["ZZ2024J20"]}]},{"name":"High-level cultivation projects of Minnan Normal University","award":["FBJY20230170"],"award-info":[{"award-number":["FBJY20230170"]}]},{"name":"High-level cultivation projects of Minnan Normal University","award":["2020CGZH0009"],"award-info":[{"award-number":["2020CGZH0009"]}]},{"name":"High-level cultivation projects of Minnan Normal University","award":["XQZX20240032"],"award-info":[{"award-number":["XQZX20240032"]}]},{"name":"Nature Science Foundation of Zhangzhou","award":["62376114"],"award-info":[{"award-number":["62376114"]}]},{"name":"Nature Science Foundation of Zhangzhou","award":["62372488"],"award-info":[{"award-number":["62372488"]}]},{"name":"Nature Science Foundation of Zhangzhou","award":["MSGJB2021007"],"award-info":[{"award-number":["MSGJB2021007"]}]},{"name":"Nature Science Foundation of Zhangzhou","award":["ZZ2024J20"],"award-info":[{"award-number":["ZZ2024J20"]}]},{"name":"Nature Science Foundation of Zhangzhou","award":["FBJY20230170"],"award-info":[{"award-number":["FBJY20230170"]}]},{"name":"Nature Science Foundation of Zhangzhou","award":["2020CGZH0009"],"award-info":[{"award-number":["2020CGZH0009"]}]},{"name":"Nature Science Foundation of Zhangzhou","award":["XQZX20240032"],"award-info":[{"award-number":["XQZX20240032"]}]},{"name":"Fujian Province Undergraduate Education and Teaching Research Project","award":["62376114"],"award-info":[{"award-number":["62376114"]}]},{"name":"Fujian Province Undergraduate Education and Teaching Research Project","award":["62372488"],"award-info":[{"award-number":["62372488"]}]},{"name":"Fujian Province Undergraduate Education and Teaching Research Project","award":["MSGJB2021007"],"award-info":[{"award-number":["MSGJB2021007"]}]},{"name":"Fujian Province Undergraduate Education and Teaching Research Project","award":["ZZ2024J20"],"award-info":[{"award-number":["ZZ2024J20"]}]},{"name":"Fujian Province Undergraduate Education and Teaching Research Project","award":["FBJY20230170"],"award-info":[{"award-number":["FBJY20230170"]}]},{"name":"Fujian Province Undergraduate Education and Teaching Research Project","award":["2020CGZH0009"],"award-info":[{"award-number":["2020CGZH0009"]}]},{"name":"Fujian Province Undergraduate Education and Teaching Research Project","award":["XQZX20240032"],"award-info":[{"award-number":["XQZX20240032"]}]},{"name":"Karamay Sci.&amp; Tech. Research Project","award":["62376114"],"award-info":[{"award-number":["62376114"]}]},{"name":"Karamay Sci.&amp; Tech. Research Project","award":["62372488"],"award-info":[{"award-number":["62372488"]}]},{"name":"Karamay Sci.&amp; Tech. Research Project","award":["MSGJB2021007"],"award-info":[{"award-number":["MSGJB2021007"]}]},{"name":"Karamay Sci.&amp; Tech. Research Project","award":["ZZ2024J20"],"award-info":[{"award-number":["ZZ2024J20"]}]},{"name":"Karamay Sci.&amp; Tech. Research Project","award":["FBJY20230170"],"award-info":[{"award-number":["FBJY20230170"]}]},{"name":"Karamay Sci.&amp; Tech. Research Project","award":["2020CGZH0009"],"award-info":[{"award-number":["2020CGZH0009"]}]},{"name":"Karamay Sci.&amp; Tech. Research Project","award":["XQZX20240032"],"award-info":[{"award-number":["XQZX20240032"]}]},{"name":"Research Foundation of China University of Petroleum-Beijing at Karamay","award":["62376114"],"award-info":[{"award-number":["62376114"]}]},{"name":"Research Foundation of China University of Petroleum-Beijing at Karamay","award":["62372488"],"award-info":[{"award-number":["62372488"]}]},{"name":"Research Foundation of China University of Petroleum-Beijing at Karamay","award":["MSGJB2021007"],"award-info":[{"award-number":["MSGJB2021007"]}]},{"name":"Research Foundation of China University of Petroleum-Beijing at Karamay","award":["ZZ2024J20"],"award-info":[{"award-number":["ZZ2024J20"]}]},{"name":"Research Foundation of China University of Petroleum-Beijing at Karamay","award":["FBJY20230170"],"award-info":[{"award-number":["FBJY20230170"]}]},{"name":"Research Foundation of China University of Petroleum-Beijing at Karamay","award":["2020CGZH0009"],"award-info":[{"award-number":["2020CGZH0009"]}]},{"name":"Research Foundation of China University of Petroleum-Beijing at Karamay","award":["XQZX20240032"],"award-info":[{"award-number":["XQZX20240032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>With the development of intelligent technology, data in practical applications show exponential growth in quantity and scale. Extracting the most distinguished attributes from complex datasets becomes a crucial problem. The existing attribute reduction approaches focus on the correlation between attributes and labels without considering the redundancy. To address the above problem, we propose an ensemble approach based on an incremental information level and improved evidence theory for attribute reduction (IILE). Firstly, the incremental information level reduction measure comprehensively assesses attributes based on reduction capability and redundancy level. Then, an improved evidence theory and approximate reduction methods are employed to fuse multiple reduction results, thereby obtaining an approximately globally optimal and a most representative subset of attributes. Eventually, using different metrics, experimental comparisons are performed on eight datasets to confirm that our proposal achieved better than other methods. The results show that our proposal can obtain more relevant attribute sets by using the incremental information level and improved evidence theory.<\/jats:p>","DOI":"10.3390\/e27010094","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T08:46:26Z","timestamp":1737449186000},"page":"94","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Novel Ensemble Approach with Incremental Information Level and Improved Evidence Theory for Attribute Reduction"],"prefix":"10.3390","volume":"27","author":[{"given":"Peng","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou 363000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou 363000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziwen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou 363000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6600-7933","authenticated-orcid":false,"given":"Baoya","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou 363000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7470-3011","authenticated-orcid":false,"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou 363000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2779-6990","authenticated-orcid":false,"given":"Ziqiong","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou 363000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhehan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"},{"name":"Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Zhangzhou 363000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"key":"ref_1","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2008.05.006","article-title":"Dimensionality reduction based on rough set theory: A review","volume":"9","author":"Thangavel","year":"2009","journal-title":"Appl. Soft Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.artint.2010.04.018","article-title":"Positive approximation: An accelerator for attribute reduction in rough set theory","volume":"174","author":"Qian","year":"2010","journal-title":"Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/BF01001956","article-title":"Rough sets","volume":"11","author":"Pawlak","year":"1982","journal-title":"Int. J. Comput. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.ijar.2007.05.019","article-title":"Probabilistic rough set approximations","volume":"49","author":"Yao","year":"2008","journal-title":"Int. J. Approx. Reason."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3577","DOI":"10.1016\/j.ins.2008.05.024","article-title":"Neighborhood rough set based heterogeneous feature subset selection","volume":"178","author":"Hu","year":"2008","journal-title":"Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/03081079008935107","article-title":"Rough fuzzy sets and fuzzy rough sets","volume":"17","author":"Dubois","year":"1990","journal-title":"Int. J. Gen. Syst."},{"key":"ref_8","unstructured":"Ahmed, H., and Nandi, A.K. (2020). Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines, John Wiley & Sons."},{"key":"ref_9","first-page":"1205","article-title":"Efficient feature selection via analysis of relevance and redundancy","volume":"5","author":"Yu","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_10","unstructured":"Tang, J., Alelyani, S., and Liu, H. (2014). Feature selection for classification: A review. Data Classification: Algorithms and Applications, CRC Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1016\/j.procs.2022.01.112","article-title":"An Evidence theory and data fusion based classification method for decision making","volume":"199","author":"Meng","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.knosys.2017.03.011","article-title":"Evidence fusion-based framework for condition evaluation of complex electromechanical system in process industry","volume":"124","author":"Jiang","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3670","DOI":"10.1109\/TKDE.2024.3369719","article-title":"Belief Re\u00b4 nyi Divergence of Divergence and Its Application in Time Series Classification","volume":"36","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14709","DOI":"10.1109\/TPAMI.2023.3310594","article-title":"Higher order fractal belief R\u00e9nyi divergence with its applications in pattern classification","volume":"45","author":"Huang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8297","DOI":"10.1109\/TKDE.2023.3342907","article-title":"Fractal belief R\u00e9nyi divergence with its applications in pattern classification","volume":"36","author":"Huang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"134","DOI":"10.62762\/CJIF.2024.999646","article-title":"Complex Evidence Theory for Multisource Data Fusion","volume":"1","author":"Xiao","year":"2024","journal-title":"Chin. J. Inf. Fusion"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2022.09.074","article-title":"Robust unsupervised feature selection via sparse and minimum-redundant subspace learning with dual regularization","volume":"2022","author":"Zeng","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1109\/TCYB.2021.3112203","article-title":"Interactive and complementary feature selection via fuzzy multigranularity uncertainty measures","volume":"53","author":"Wan","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_19","unstructured":"Pawlak, Z. (2012). Rough Sets Theoretical Aspects of Reasoning About Data, Springer Science & Business Medi."},{"key":"ref_20","first-page":"54","article-title":"Attribute reduction on weighted decision table","volume":"56","author":"Li","year":"2020","journal-title":"Comput. Eng. Appl."},{"key":"ref_21","first-page":"275","article-title":"FSSD Algorithm Based on Ensemble Feature Selection","volume":"31","author":"Zhang","year":"2022","journal-title":"Comput. Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fang, Y., Cao, X., and Wang, X. (2022). Hypersphere Neighborhood Rough Set for Rapid Attribute Reduction. Advances in Knowledge Discovery and Data Mining, Springer International Publishing.","DOI":"10.1007\/978-3-031-05936-0_13"},{"key":"ref_23","first-page":"2701","article-title":"Attribute reduction algorithm based on cluster granulation and divergence among clusters","volume":"42","author":"Li","year":"2022","journal-title":"J. Comput. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"119753","DOI":"10.1016\/j.ins.2023.119753","article-title":"Feature selection based on fuzzy combination entropy considering global and local feature correlation","volume":"652","author":"Dai","year":"2024","journal-title":"Inf. Sci."},{"key":"ref_25","first-page":"8","article-title":"Hybrid Improved Flower Pollination Algorithm and Gray Wolf Algorithm for Feature Selection","volume":"49","author":"Kang","year":"2022","journal-title":"Comput. Sci."},{"key":"ref_26","first-page":"1842","article-title":"Feature selection for imbalanced data based on neighborhood tolerance mutual information and whale optimization algorithm","volume":"43","author":"Sun","year":"2023","journal-title":"J. Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1109\/TKDE.2020.2997039","article-title":"GBNRS: A novel rough set algorithm for fast adaptive attribute reduction in classification","volume":"34","author":"Xia","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_28","unstructured":"Guo, X., and Li, H. (2024). Attribute reduction algorithm of rough sets based on spatial optimization. arXiv."},{"key":"ref_29","first-page":"13","article-title":"Hybrid Feature Selection Algorithm Combining Information Gain Ratio and Genetic Algorithm","volume":"33","author":"Xu","year":"2022","journal-title":"J. Softw."},{"key":"ref_30","first-page":"175","article-title":"Class-specific attribute reduct and its heuristic algorithm of neighborhood approximation condition-entropy","volume":"56","author":"Mou","year":"2020","journal-title":"Comput. Eng. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.ijar.2022.09.007","article-title":"Parameterized maximum-entropy-based three-way approximate attribute reduction","volume":"151","author":"Gao","year":"2022","journal-title":"Int. J. Approx. Reason."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107418","DOI":"10.1016\/j.knosys.2021.107418","article-title":"TCIC_FS: Total correlation information coefficient-based feature selection method for high-dimensional data","volume":"231","author":"Qiu","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4031","DOI":"10.1109\/TCYB.2019.2923430","article-title":"Feature selection based on neighborhood self-information","volume":"50","author":"Wang","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"17727","DOI":"10.1007\/s10489-022-04398-z","article-title":"Maximum relevance minimum redundancy-based feature selection using rough mutual information in adaptive neighborhood rough sets","volume":"53","author":"Qu","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_35","first-page":"2","article-title":"Shannon entropy, Renyi entropy, and information","volume":"9","author":"Bromiley","year":"2024","journal-title":"Stat. Inf."},{"key":"ref_36","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"12591","DOI":"10.1007\/s11227-023-05138-x","article-title":"Multimodal feature selection from microarray data based on Dempster\u2013Shafer evidence fusion","volume":"79","author":"Nekouie","year":"2023","journal-title":"J. Supercomput."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/1\/94\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:32:09Z","timestamp":1759919529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/1\/94"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,20]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["e27010094"],"URL":"https:\/\/doi.org\/10.3390\/e27010094","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,20]]}}}