{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T15:26:11Z","timestamp":1783092371963,"version":"3.54.6"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,9]],"date-time":"2022-06-09T00:00:00Z","timestamp":1654732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["82090052"],"award-info":[{"award-number":["82090052"]}]},{"name":"National Natural Science Foundation of China","award":["2021SHZDZX0103"],"award-info":[{"award-number":["2021SHZDZX0103"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["82090052"],"award-info":[{"award-number":["82090052"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX0103"],"award-info":[{"award-number":["2021SHZDZX0103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Feature selection can efficiently improve classification accuracy and reduce the dimension of datasets. However, feature selection is a challenging and complex task that requires a high-performance optimization algorithm. In this paper, we propose an enhanced binary bat algorithm (EBBA) which is originated from the conventional binary bat algorithm (BBA) as the learning algorithm in a wrapper-based feature selection model. First, we model the feature selection problem and then transfer it as a fitness function. Then, we propose an EBBA for solving the feature selection problem. In EBBA, we introduce the L\u00e9vy flight-based global search method, population diversity boosting method and chaos-based loudness method to improve the BA and make it more applicable to feature selection problems. Finally, the simulations are conducted to evaluate the proposed EBBA and the simulation results demonstrate that the proposed EBBA outmatches other comparison benchmarks. Moreover, we also illustrate the effectiveness of the proposed improved factors by tests.<\/jats:p>","DOI":"10.3390\/fi14060178","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:22:39Z","timestamp":1654820559000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and L\u00e9vy Flight for Feature Selection"],"prefix":"10.3390","volume":"14","author":[{"given":"Jinghui","family":"Feng","sequence":"first","affiliation":[{"name":"Academy for Engineering & Technology, Fudan University, Shanghai 200433, China"},{"name":"Academy for Electromechanical, Changchun Polytechnic, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haopeng","family":"Kuang","sequence":"additional","affiliation":[{"name":"Academy for Engineering & Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academy for Engineering & Technology, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108664","DOI":"10.1016\/j.comnet.2021.108664","article-title":"Proactive and intelligent evaluation of big data queries in edge clouds with materialized views","volume":"203","author":"Xia","year":"2022","journal-title":"Comput. 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