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Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. Feature selection is necessary in the process of constructing the model to reduce time and space consumption. Therefore, a feature selection model based on prior knowledge and rough set is proposed. Pathway knowledge is used to select feature subsets, and rough set based on intersection neighborhood is then used to select important feature in each subset, since it can select features without redundancy and deals with numerical features directly. In order to improve the diversity among base classifiers and the efficiency of classification, it is necessary to select part of base classifiers. Classifiers are grouped into several clusters by k\u2010means clustering using the proposed combination distance of Kappa\u2010based diversity and accuracy. The base classifier with the best classification performance in each cluster will be selected to generate the final ensemble model. Experimental results on three <jats:italic>Arabidopsis thaliana<\/jats:italic> stress response datasets showed that the proposed method achieved better classification performance than existing ensemble models.<\/jats:p>","DOI":"10.1155\/2021\/8358921","type":"journal-article","created":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T21:20:35Z","timestamp":1637788835000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Ensemble Classification Method for High\u2010Dimensional Data Using Neighborhood Rough Set"],"prefix":"10.1155","volume":"2021","author":[{"given":"Jing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9764-3798","authenticated-orcid":false,"given":"Guang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6534-2210","authenticated-orcid":false,"given":"Jiaquan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2227-1296","authenticated-orcid":false,"given":"Chuanwen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2012.09.006"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcbb.2012.103"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2015.10.008"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2013.02.013"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107933"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107034"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf01001956"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1080\/10798587.1996.10750660"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.09.013"},{"key":"e_1_2_9_10_2","first-page":"794","article-title":"Gene selection for cancer classification in microarray data","volume":"46","author":"Zhang L. 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