Classification by ALH-Fast Algorithm

作     者：Tao Yang Vojislav Kecman Longbing Cao 

作者机构：Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 Australia Department of Computer Science The Virginia Commonwealth University (VCU) Richmond VA 23284-3068 USA 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2010年第15卷第3期

页      面：275-280页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 13[艺术学] 08[工学] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

主　　题：classification adaptive local hyperplane (ALH) decision tree 

摘      要：The adaptive local hyperplane （ALH） algorithm is a very recently proposed classifier, which has been shown to perform better than many other benchmarking classifiers including support vector machine （SVM）, K-nearest neighbor （KNN）, linear discriminant analysis （LDA）, and K-local hyperplane distance nearest neighbor （HKNN） algorithms. Although the ALH algorithm is well formulated and despite the fact that it performs well in practice, its scalability over a very large data set is limited due to the online distance computations associated with all training instances. In this paper, a novel algorithm, called ALH-Fast and obtained by combining the classification tree algorithm and the ALH, is proposed to reduce the computational load of the ALH algorithm. The experiment results on two large data sets show that the ALH-Fast algorithm is both much faster and more accurate than the ALH algorithm.