{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:43:18Z","timestamp":1750308198239,"version":"3.41.0"},"reference-count":8,"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>\n            In this paper we describe the winning model for the performance measure \"lowest ranked homologous sequence\" (RKL). This was a subtask of the Protein Homology Prediction task of the KDD Cup 2004. The goal was to predict protein homology for different performance metrics. The given data was organized in blocks, each of which corresponds to a specific native sequence. The two metrics average precision (APR) and RKL explicitly make use of this block structure. Our solution consists of two parts. The first one is a global classification SVM not aware of the block structure. The second part is a\n            <jats:italic>k-NearestNeighbor<\/jats:italic>\n            scheme for block similarity, used to train ranking SVMs on the fly. Furthermore, we sketch our approach to optimize the root-mean-squared-error and report some alternative solutions that turned out to be suboptimal.\n          <\/jats:p>","DOI":"10.1145\/1046456.1046477","type":"journal-article","created":{"date-parts":[[2007,1,17]],"date-time":"2007-01-17T18:32:02Z","timestamp":1169058722000},"page":"128-131","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["KDD-Cup 2004"],"prefix":"10.1145","volume":"6","author":[{"given":"Christophe","family":"Foussette","sequence":"first","affiliation":[{"name":"University of Dortmund, Dortmund, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Hakenjos","sequence":"additional","affiliation":[{"name":"University of Dortmund, Dortmund, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Scholz","sequence":"additional","affiliation":[{"name":"University of Dortmund, Dortmund, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2004,12]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"The New S Language","author":"Becker Richard A.","year":"1988","unstructured":"Richard A. Becker , John M. Chambers , and Allan R. Wilks . The New S Language . Chapman & Hall , London , 1988 . Richard A. Becker, John M. Chambers, and Allan R. Wilks. The New S Language. Chapman & Hall, London, 1988."},{"key":"e_1_2_1_2_1","volume-title":"An Introduction to Support Vector Machines and other kernel-based learning methods","author":"Shawe-Taylor N.","year":"2000","unstructured":"Cristianini, N. and Shawe-Taylor , J. , An Introduction to Support Vector Machines and other kernel-based learning methods , Cambridge Press , 2000 . Cristianini, N. and Shawe-Taylor, J., An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge Press, 2000."},{"key":"e_1_2_1_3_1","volume-title":"Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning","author":"Joachims T.","year":"1999","unstructured":"T. Joachims , Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning , B. Schoelkopf and C. Burges and A. Smola (ed.), MIT-Press , 1999 . T. Joachims, Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Schoelkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999."},{"doi-asserted-by":"publisher","key":"e_1_2_1_4_1","DOI":"10.1145\/775047.775067"},{"key":"e_1_2_1_5_1","volume-title":"LLWA 03 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitaet","author":"Klinkberg Ingo","year":"2003","unstructured":"Mierswa, Ingo and Klinkberg , Ralf and Fischer , Simon and Ritthoff , Oliver. A Flexible Platform for Knowledge Discovery Experiments: YALE -- Yet Another Learning Environment . In LLWA 03 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitaet , 2003 . Mierswa, Ingo and Klinkberg, Ralf and Fischer, Simon and Ritthoff, Oliver. A Flexible Platform for Knowledge Discovery Experiments: YALE -- Yet Another Learning Environment. In LLWA 03 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitaet, 2003."},{"key":"e_1_2_1_6_1","volume-title":"Witten and Eibe Frank","author":"Ian","year":"2000","unstructured":"Ian H. Witten and Eibe Frank , Data Mining : Practical machine learning tools with Java implementations, Morgan Kaufmann , San Francisco, 2000 . Ian H. Witten and Eibe Frank, Data Mining: Practical machine learning tools with Java implementations, Morgan Kaufmann, San Francisco, 2000."},{"doi-asserted-by":"publisher","key":"e_1_2_1_7_1","DOI":"10.1006\/jcss.1997.1504"},{"unstructured":"J. Diez J. J. del Coz O. Luaces and A. Bahamonde. A Clustering Algorithm to Find Groups With Homogeneous Preferences.  J. Diez J. J. del Coz O. Luaces and A. Bahamonde. A Clustering Algorithm to Find Groups With Homogeneous Preferences.","key":"e_1_2_1_8_1"}],"container-title":["ACM SIGKDD Explorations Newsletter"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1046456.1046477","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1046456.1046477","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T16:24:57Z","timestamp":1750263897000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1046456.1046477"}},"subtitle":["protein homology task"],"short-title":[],"issued":{"date-parts":[[2004,12]]},"references-count":8,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2004,12]]}},"alternative-id":["10.1145\/1046456.1046477"],"URL":"https:\/\/doi.org\/10.1145\/1046456.1046477","relation":{},"ISSN":["1931-0145","1931-0153"],"issn-type":[{"type":"print","value":"1931-0145"},{"type":"electronic","value":"1931-0153"}],"subject":[],"published":{"date-parts":[[2004,12]]},"assertion":[{"value":"2004-12-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}