{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T10:19:29Z","timestamp":1648549169765},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2008,2]]},"abstract":"<jats:p> This paper aims to improve probability-based ranking (e.g. AUC) under decision-tree paradigm. We observe the fact that probability-based ranking is to sort samples in terms of their class probabilities. Therefore, ranking is a relative evaluation metric among those samples. This motivates us to use a lazy learner to explicitly yield a set of unique class probabilities for a testing sample based on its similarities to the training samples within its neighborhood. We embed lazy learners at the leaves of a decision tree to give class probability assignments. This results in the first model, named Lazy Distance-based Tree (LDTree). Then we further improve this model by continuing to grow the tree for the second time, and call the resulting model Eager Distance-based Tree (EDTree). In addition to the benefits of lazy learning, EDTree also takes advantage of the finer resolution of a large tree structure. We compare our models with C4.5, C4.4 and their variants in AUC on a large suite of UCI sample sets. The improvement shows that our method follows a new path that leads to better ranking performance. <\/jats:p>","DOI":"10.1142\/s0218213008003819","type":"journal-article","created":{"date-parts":[[2008,3,4]],"date-time":"2008-03-04T11:05:40Z","timestamp":1204628740000},"page":"139-158","source":"Crossref","is-referenced-by-count":1,"title":["LAZY LEARNER ON DECISION TREE FOR RANKING"],"prefix":"10.1142","volume":"17","author":[{"given":"YUHONG","family":"YAN","sequence":"first","affiliation":[{"name":"Institute for Information Technology, National Research of Council of Canada, Fredericton, NB E3B 5X9, Canada"}]},{"given":"HAN","family":"LIANG","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3, Canada"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","volume":"36","author":"Bauer E.","journal-title":"Artificial Intelligence"},{"key":"rf4","volume":"10","author":"Cohen W. W.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"rf8","volume":"45","author":"Hand D. J.","journal-title":"Machine Learning"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5"},{"key":"rf12","volume":"52","author":"Nadeau C.","journal-title":"Machine Learning"},{"key":"rf13","volume":"52","author":"Provost F. J.","journal-title":"Machine Learning"},{"key":"rf15","volume":"2","author":"Quinlan J. R.","journal-title":"Machine Learning"},{"key":"rf16","volume-title":"C4.5: Programs for Machine Learning","author":"Quinlan J. R.","year":"1993"},{"key":"rf17","first-page":"197","volume":"5","author":"Schapire Robert E.","journal-title":"Machine Learning"},{"key":"rf20","volume-title":"Data Mining\u2013Practical Machine Learning Tools and Techniques with Java Implementation","author":"Witten I. H.","year":"2000"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213008003819","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T17:10:03Z","timestamp":1565197803000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213008003819"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,2]]},"references-count":10,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2008,2]]}},"alternative-id":["10.1142\/S0218213008003819"],"URL":"https:\/\/doi.org\/10.1142\/s0218213008003819","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,2]]}}}