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The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra\u2010high data dimensions, the number of samples available for analysis is too small, resulting in the production of high\u2010dimensional small sample data. High\u2010dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high\u2010dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF\u2010RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high\u2010dimensional small sample data sets.<\/jats:p>","DOI":"10.1155\/2021\/3597051","type":"journal-article","created":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T02:18:30Z","timestamp":1632536310000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Feature Selection and Feature Stability Measurement Method for High\u2010Dimensional Small Sample Data Based on Big Data Technology"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6862-6884","authenticated-orcid":false,"given":"Chengyuan","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,9,24]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-7-3"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5689-3"},{"key":"e_1_2_8_3_2","first-page":"2601","article-title":"Summary of research on dimensionality reduction of high-dimensional data features","volume":"25","author":"Hu J.","year":"2008","journal-title":"Application Research of Computers"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcbb.2007.1006"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065705000396"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.02.008"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcbb.2012.33"},{"key":"e_1_2_8_8_2","first-page":"2433","article-title":"A survey of feature selection in the classification of high-dimensional small samples","volume":"37","author":"Wang X.","year":"2017","journal-title":"Computer Applications"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2004.11.001"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1142\/s0219720005001004"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753382129"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1074\/jbc.m010192200"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/17.6.509"},{"key":"e_1_2_8_14_2","article-title":"KEC: unique sequence search by K-mer exclusion","volume":"21","author":"Pavel B.","year":"2021","journal-title":"Bioinformatics"},{"key":"e_1_2_8_15_2","doi-asserted-by":"crossref","unstructured":"OsarehA.andShadgarB. 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