{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T13:31:27Z","timestamp":1751549487581,"version":"3.41.0"},"reference-count":7,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2004,12,1]],"date-time":"2004-12-01T00:00:00Z","timestamp":1101859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2004,12]]},"abstract":"<jats:p>This paper describes our solution for the protein homology prediction task in KDD Cup 2004 competition. This task is modeled as a supervised learning problem with multiple performance metrics. Several key characteristics make the problem both novel and challenging, including the concept of data blocks and the presence of large-scale and imbalanced training data. These features make a naive application of the traditional classification algorithms infeasible. Our approach focuses on making full use of the abundant information within the blocks, and developing a new technique for reducing and balancing training data to make the support vector machine applicable to this kind of large-scale and imbalanced learning tasks.<\/jats:p>","DOI":"10.1145\/1046456.1046475","type":"journal-article","created":{"date-parts":[[2007,1,17]],"date-time":"2007-01-17T18:32:02Z","timestamp":1169058722000},"page":"120-124","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["A block-based support vector machine approach to the protein homology prediction task in KDD Cup 2004"],"prefix":"10.1145","volume":"6","author":[{"given":"Yan","family":"Fu","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]},{"given":"Ruixiang","family":"Sun","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China"}]},{"given":"Simin","family":"He","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]},{"given":"Chunli","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]},{"given":"Haipeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]},{"given":"Shiguang","family":"Shan","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]},{"given":"Junfa","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]},{"given":"Wen","family":"Gao","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2004,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/345662"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/211359"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/572351"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4057.001.0001","volume-title":"Kernel Methods in Computational Biology","author":"Sch\u00f6lkopf B.","year":"2004","unstructured":"Sch\u00f6lkopf , B. , Tsuda , K. and Vert J . -P. 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