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This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.<\/jats:p>","DOI":"10.3389\/frai.2024.1287875","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T04:42:29Z","timestamp":1708922549000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["A distance-based kernel for classification via Support Vector Machines"],"prefix":"10.3389","volume":"7","author":[{"given":"Nazhir","family":"Amaya-Tejera","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Margarita","family":"Gamarra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge I.","family":"V\u00e9lez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"Zurek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"637","DOI":"10.14569\/IJACSA.2019.0100637","article-title":"Implementation of machine learning model to predict heart failure disease","volume":"10","author":"Alotaibi","year":"2019","journal-title":"IJACSA"},{"key":"B2","first-page":"17","article-title":"Email spam classification using hybrid approach of RBF neural network and particle swarm optimization","volume":"8","author":"Awad","year":"2016","journal-title":"Int. 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