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.
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.
