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In this paper, the authors propose an improved random forest method which uses a novel feature weighting method for subspace selection and therefore enhances classification performance over high-dimensional data. A series of experiments on 9 real life high dimensional datasets demonstrated that using a subspace size of  features where M is the total number of features in the dataset, our random forest model significantly outperforms existing random forest models.<\/p>","DOI":"10.4018\/jdwm.2012040103","type":"journal-article","created":{"date-parts":[[2012,3,29]],"date-time":"2012-03-29T09:34:44Z","timestamp":1333013684000},"page":"44-63","source":"Crossref","is-referenced-by-count":76,"title":["Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces"],"prefix":"10.4018","volume":"8","author":[{"given":"Baoxun","family":"Xu","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology Shenzhen Graduate School, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua Zhexue","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology and Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Graham","family":"Williams","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, and Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology Shenzhen Graduate School, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunming","family":"Ye","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology Shenzhen Graduate School, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jdwm.2012040103-0","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btn356"},{"key":"jdwm.2012040103-1","unstructured":"Asuncion, A., & Newman, D. 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