文章基本信息

标题：Advanced Probabilistic Binary Decision Tree Using SVM for large class problem
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作者：Anita Meshram ; Roopam Gupta ; Sanjeev Sharma 等
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2015
卷号：6
期号：2
页码：1660-1664
出版社：TechScience Publications
摘要：In this paper an algorithm of Advanced Probabilistic Binary Decision Tree (APBDT) using SVM for solving large classification problems is introduced, APBDTSVM is tested in view of the size of the databases. APBDTSVM integrates Binary Decision Tree (BDT) and Probabilistic SVM for solving multiclass classification issues. Probabilistic SVM uses standard SVM’s output and sigmoid function to map the SVM output into probabilities. SVM’s output merges with a sigmoid function, enlarge speed in decision making when combined with Binary Decision Tree (BDT). PSVM use to estimate the probability of membership to each sub-groups. APBDT-SVM lead to a dramatic improvement in recognition speed when addressing problems with large number of classes. Here Performance is evaluated in terms of classification accuracy, training and testing time by using standard UCI Machine Learning Repositories. The proposed APBDT-SVM method better performs for classification accuracy and computation time when compared to the other multiclass classification method like OaO, OaA, BDT and DAG.
关键词：Support Vector Machine; Probabilistic SVM; Binary Decision;Tree; separability measures