{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T05:02:38Z","timestamp":1766466158949,"version":"3.41.0"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2006,12,1]],"date-time":"2006-12-01T00:00:00Z","timestamp":1164931200000},"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":[[2006,12]]},"abstract":"<jats:p>\n            Classification is a well-studied problem in machine learning and data mining. Classifier performance was originally gauged almost exclusively using predictive accuracy. However, as work in the field progressed, more sophisticated measures of classifier\n            <jats:italic>utility<\/jats:italic>\n            that better represented the value of the induced knowledge were introduced. Nonetheless, most work still ignored the cost of acquiring training examples, even though this affects the\n            <jats:italic>overall<\/jats:italic>\n            utility of a classifier. In this paper we consider the costs of acquiring the training examples in the data mining process; we analyze the impact of the cost of training data on learning, identify the optimal training set size for a given data set, and analyze the performance of several progressive sampling schemes, which, given the cost of the training data, will generate classifiers that come close to maximizing the overall utility.\n          <\/jats:p>","DOI":"10.1145\/1233321.1233325","type":"journal-article","created":{"date-parts":[[2007,4,5]],"date-time":"2007-04-05T19:52:18Z","timestamp":1175802738000},"page":"31-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Maximizing classifier utility when training data is costly"],"prefix":"10.1145","volume":"8","author":[{"given":"Gary M.","family":"Weiss","sequence":"first","affiliation":[{"name":"Fordham University, Bronx, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Tian","sequence":"additional","affiliation":[{"name":"Fordham University, Bronx, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2006,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022673506211"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347126"},{"volume-title":"Proceedings of the Twentieth National Conference on Artificial Intelligence","year":"2005","author":"Hoehn B.","key":"e_1_2_1_3_1"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/11564096_20"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1089827.1089828"},{"key":"e_1_2_1_6_1","unstructured":"Newman D. J. Hettich S. Blake C. L. and Merz C. J. UCI Repository of machine learning databases {http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html}. Irvine CA: University of California Department of Information and Computer Science. 1998.  Newman D. J. Hettich S. Blake C. L. and Merz C. J. UCI Repository of machine learning databases {http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html}. Irvine CA: University of California Department of Information and Computer Science. 1998."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312188"},{"key":"e_1_2_1_8_1","unstructured":"Quinlan J. R. C4.5: Programs for Machine Learning. San Mateo CA. Morgan Kaufmann. 1993.   Quinlan J. R. C4.5: Programs for Machine Learning. San Mateo CA. Morgan Kaufmann. 1993."},{"volume-title":"P. Types of Cost in Inductive Concept Learning. Workshop on Cost-Sensitive Learning at the 17th International Conference on Machine Learning","year":"2000","author":"Turney","key":"e_1_2_1_9_1"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/951949.952087"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622434.1622445"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","DOI":"10.1145\/1089827","volume-title":"Proceedings of the First International Workshop on Utility-Based Data Mining (editors), ACM Press","author":"Weiss G. M.","year":"2005"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/844380.844719"}],"container-title":["ACM SIGKDD Explorations Newsletter"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1233321.1233325","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1233321.1233325","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T14:51:51Z","timestamp":1750258311000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1233321.1233325"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,12]]},"references-count":13,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2006,12]]}},"alternative-id":["10.1145\/1233321.1233325"],"URL":"https:\/\/doi.org\/10.1145\/1233321.1233325","relation":{},"ISSN":["1931-0145","1931-0153"],"issn-type":[{"type":"print","value":"1931-0145"},{"type":"electronic","value":"1931-0153"}],"subject":[],"published":{"date-parts":[[2006,12]]},"assertion":[{"value":"2006-12-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}