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In this study, we investigate whether the joint use of 1) feature selection techniques, such as Chi-square, Tree-based Feature Selection, Pearson\u2019s Correlation, LASSO, Low Variance, and Recursive Feature Elimination, 2) outlier detection methods such as Isolation-Forest, and 3) Cross-Validation techniques lead to improving the accuracy in multiclass classification in machine learning. Specifically, we address the classification of patterns representing the activation state of cell signaling components into classes that symbolize the different cellular processes triggered in cancer cells. The results presented in this work have shown an accuracy increase with up to 80% fewer input features by only using 3 out of the 16 original descriptors.<\/jats:p>","DOI":"10.3233\/ida-215826","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T18:20:20Z","timestamp":1647973220000},"page":"481-500","source":"Crossref","is-referenced-by-count":4,"title":["Improving the accuracy of multiclass classification in machine learning: A case study in a cell signaling dataset"],"prefix":"10.1177","volume":"26","author":[{"given":"Pedro Pablo","family":"Gonz\u00e1lez-P\u00e9rez","sequence":"first","affiliation":[{"name":"Departamento de Matem\u00e1ticas Aplicadas y Sistemas, Universidad Aut\u00f3noma Metropolitana-Cuajimalpa, Ciudad de M\u00e9xico, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M\u00e1ximo Eduardo","family":"S\u00e1nchez-Guti\u00e9rrez","sequence":"additional","affiliation":[{"name":"Colegio de Ciencia y Tecnolog\u00eda, Universidad Aut\u00f3noma de la Ciudad de M\u00e9xico, Ciudad de M\u00e9xico, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-215826_ref1","doi-asserted-by":"crossref","unstructured":"V. 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