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Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>\n            The lack of interpretability often makes black-box models challenging to be applied in many practical domains. For this reason, the current work, from the black-box model input port, proposes to incorporate data-mined knowledge into the black-box soft-margin SVM model to enhance accuracy and interpretability. The concept and incorporation mechanism of data-mined knowledge are successively developed, based on which a\n            <jats:bold>partially interpretable soft-margin SVM (<\/jats:bold>\n            <jats:italic>pTsm<\/jats:italic>\n            <jats:bold>-SVM)<\/jats:bold>\n            optimization model is designed and then solved through reformulating the optimization problem as standard quadratic programming. An algorithm for mining linear positive (negative) class knowledge from general data sets is also proposed, which generates a linear two-dimensional discriminative rule with specificity (sensitivity) equal to 1 and the highest possible sensitivity (specificity) among all two-dimensional feature spaces. The knowledge-integrated\n            <jats:italic>pTsm<\/jats:italic>\n            -SVM works by achieving a good trade-off among the \u201clarge margin\u201d, \u201chigh specificity\u201d, and \u201chigh sensitivity\u201d. Our experimental results on eight UCI datasets demonstrate the superiority of the proposed\n            <jats:italic>pTsm<\/jats:italic>\n            -SVM over the standard soft-margin SVM both in terms of accuracy and interpretability.\n          <\/jats:p>","DOI":"10.1145\/3548775","type":"journal-article","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T11:14:05Z","timestamp":1657797245000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Incorporation of Data-Mined Knowledge into Black-Box SVM for Interpretability"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-2027","authenticated-orcid":false,"given":"Shaohan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9030-2042","authenticated-orcid":false,"given":"Chuanhou","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3034-3576","authenticated-orcid":false,"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/0885-064X(90)90006-Y"},{"key":"e_1_3_2_3_2","first-page":"28","volume-title":"Proceedings of International Multi Conference of Engineers and Computer Scientists, Hong Kong","volume":"1","author":"Adeli Ali","year":"2010","unstructured":"Ali Adeli and Mehdi Neshat. 2010. 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