{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:02:16Z","timestamp":1770750136211,"version":"3.50.0"},"reference-count":10,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2015,10,28]],"date-time":"2015-10-28T00:00:00Z","timestamp":1445990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2015,11,27]]},"abstract":"<jats:p>Fuzzy rule-based classification systems have been used extensively in data mining. This paper proposes a fuzzy rule-based classification algorithm based on a quantum ant optimization algorithm. A method of generating the hierarchical rules with different granularity hybridization is used to generate the initial rule set. This method can obtain an original rule set with a smaller number of rules. The modified quantum ant optimization algorithm is used to generate the optimal individual. Compared to other similar algorithms, the algorithm proposed in this paper demonstrates higher classification accuracy and a higher convergence rate. The algorithm is proved to be convergent on theory. Some experiments have been conducted on the algorithm, and the results proved that the algorithm is feasible.<\/jats:p>","DOI":"10.3233\/ifs-151935","type":"journal-article","created":{"date-parts":[[2015,12,9]],"date-time":"2015-12-09T14:26:12Z","timestamp":1449671172000},"page":"2365-2371","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Rule-based fuzzy classifier based on quantum ant optimization algorithm"],"prefix":"10.1177","volume":"29","author":[{"given":"Jue","family":"Wu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"},{"name":"School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China"}]},{"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China"}]},{"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China"}]},{"given":"Changjiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Wenzhou-Kean University, Wenzhou, China"}]},{"given":"Zhihui","family":"Li","sequence":"additional","affiliation":[{"name":"Hypervelocity Aerodynamics Institute of China, Aerodynamics Research and Development Center Mianyang, China"}]}],"member":"179","published-online":{"date-parts":[[2015,10,28]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"765","article-title":"A genetic algorithm for generating optimal fuzzy rules","volume":"7","author":"Lim CG","year":"2003","unstructured":"Lim CG, Jung YM, Kim EK2003A genetic algorithm for generating optimal fuzzy rulesInternational Journal of KIMICS7765778","journal-title":"International Journal of 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