期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2012
卷号：2
期号：10
出版社：S.S. Mishra
摘要：Many real world data mining applications involve classification of rare cases from imbalanced data sets. It is a common problem in many domains such as detecting oil spills from satellite images, predicting telecommunication equipment failures and finding associations between infrequently purchased supermarket items . Rare cases warrant special attention because they pose significant problems for data mining algorithms. Classifying data using Boosting algorithm performs supervised learning which is known as machine learning meta -algorithm. Boosting methods are commonly used to detect objects or persons in videoconference, security system, etc. This paper gives an overview of boosting based algorithms used for classification namely LPBoost, TotalBoost, BrownBoost, GentleBoost, LogitBoost, MadaBoost, RankBoost.
关键词：AUC; Bootstrapping; Bagging; cost-sensitive learning; Precision; Rare cases; small disjuncts.
