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Preprocessing step involves image segmentation for background elimination, de-noising and image enhancement. A combination of color, local binary pattern (LBP), histogram of oriented gradients (HOG), and wavelet features forms the feature vector. An ensemble feature reduction technique is used to reduce the attribute size. Performances of the system using combined as well as reduced feature sets are evaluated using seven popular classifiers. Among the classifiers, wavelet kernel extreme learning machine (ELM) showed higher classification accuracy of 96.65% in genus level and polynomial kernel ELM showed an accuracy of 92.42% in species level with the reduced feature set. <\/jats:p>","DOI":"10.1142\/s0218001420500287","type":"journal-article","created":{"date-parts":[[2019,12,13]],"date-time":"2019-12-13T08:16:24Z","timestamp":1576224984000},"page":"2050028","source":"Crossref","is-referenced-by-count":6,"title":["Genus and Species-Level Classification of Wrasse Fishes Using Multidomain Features and Extreme Learning Machine Classifier"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4921-2584","authenticated-orcid":false,"given":"Jisha Anu","family":"Jose","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Rajiv Gandhi Institute of Technology, Kottayam, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. 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