Classification by ALH-Fast Algorithm

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...

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Bibliographic Details
Published in:Tsinghua science and technology 2010-06, Vol.15 (3), p.275-280
Main Authors: Yang, Tao, Kecman, Vojislav, Cao, Longbing
Format: Article
Language:English
Subjects:
adaptive local hyperplane (ALH)
Algorithms
Classification
Classifiers
Computation
decision tree
Discriminant analysis
Hyperplanes
On-line systems
Support vector machines
分类器
可扩展性
快速算法
支持向量机
数据集
线性判别分析
超平面
距离计算
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Summary: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.
ISSN:1007-0214
1878-7606
1007-0214
DOI:10.1016/S1007-0214(10)70061-4