{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T19:32:39Z","timestamp":1649014359095},"reference-count":14,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2010,2]]},"abstract":"<jats:p> In machine learning and data mining, traditional learning models aim for high classification accuracy. However, accurate class probability prediction is more desirable than classification accuracy in many practical applications, such as medical diagnosis. Although it is known that decision trees can be adapted to be class probability estimators in a variety of approaches, and the resulting models are uniformly called Probability Estimation Trees (PETs), the performances of these PETs in class probability estimation, have not yet been investigated. We begin our research by empirically studying PETs in terms of class probability estimation, measured by Log Conditional Likelihood (LCL). We also compare a PET called C4.4 with other representative models, including Na\u00efve Bayes, Na\u00efve Bayes Tree, Bayesian Network, KNN and SVM, in LCL. From our experiments, we draw several valuable conclusions. First, among various tree-based models, C4.4 is the best in yielding precise class probability prediction measured by LCL. We provide an explanation for this and reveal the nature of LCL. Second, compared with non tree-based models, C4.4 also performs best. Finally, LCL does not dominate another well-established relevant metric \u2014 AUC, which suggests that different decision-tree learning models should be used for different objectives. Our experiments are conducted on the basis of 36 UCI sample sets. We run all the models within a machine learning platform \u2014 Weka. <\/jats:p><jats:p> We also explore an approach to improve the class probability estimation of Na\u00efve Bayes Tree. We propose a greedy and recursive learning algorithm, where at each step, LCL is used as the scoring function to expand the decision tree. The algorithm uses Na\u00efve Bayes created at leaves to estimate class probabilities of test samples. The whole tree encodes the posterior class probability in its structure. One benefit of improving class probability estimation is that both classification accuracy and AUC can be possibly scaled up. We call the new model LCL Tree (LCLT). Our experiments on 33 UCI sample sets show that LCLT outperforms all state-of-the-art learning models, such as Na\u00efve Bayes Tree, significantly in accurate class probability prediction measured by LCL, as well as in classification accuracy and AUC. <\/jats:p>","DOI":"10.1142\/s0218001410007877","type":"journal-article","created":{"date-parts":[[2010,3,10]],"date-time":"2010-03-10T11:37:05Z","timestamp":1268221025000},"page":"117-151","source":"Crossref","is-referenced-by-count":2,"title":["LEARNING DECISION TREES WITH LOG CONDITIONAL LIKELIHOOD"],"prefix":"10.1142","volume":"24","author":[{"given":"HAN","family":"LIANG","sequence":"first","affiliation":[{"name":"Department of Computing Science University of Alberta, Edmonton Alberta T6G 2E1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YUHONG","family":"YAN","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Software Engineering, Concordia University, Montreal Quebec H3G 1M8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HARRY","family":"ZHANG","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick E3B 5A3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","volume":"36","author":"Bauer E.","journal-title":"Artif. Intell."},{"key":"rf3","volume":"20","author":"Cortes C.","journal-title":"Mach. Learn."},{"key":"rf4","volume":"13","author":"Dawid A. P.","journal-title":"Ann. Stat."},{"key":"rf9","volume":"29","author":"Geiger D.","journal-title":"Mach. Learn."},{"key":"rf12","volume":"45","author":"Hand D. J.","journal-title":"Mach. Learn."},{"key":"rf14","volume":"17","author":"Huang J.","journal-title":"IEEE Trans. Knowl. Data Engin."},{"key":"rf19","volume":"16","author":"Kurgan L.","journal-title":"IEEE Trans. Knowl. Data Engin."},{"key":"rf21","volume":"5","author":"Lin C. J.","journal-title":"Mach. Learn."},{"key":"rf23","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-3242-6"},{"key":"rf24","volume":"52","author":"Nadeau C.","journal-title":"Mach. Learn."},{"key":"rf25","volume-title":"Probabilistic Reasoning in Intelligent Systems","author":"Pearl J.","year":"1988"},{"key":"rf26","volume":"52","author":"Provost F. J.","journal-title":"Mach. 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H.","year":"2000"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001410007877","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T02:17:04Z","timestamp":1565144224000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001410007877"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,2]]},"references-count":14,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2010,2]]}},"alternative-id":["10.1142\/S0218001410007877"],"URL":"https:\/\/doi.org\/10.1142\/s0218001410007877","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,2]]}}}