期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 2, 页码 1587-1592出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2007.11.051
关键词
Hybrid systems; C4.5 Decision tree classifier; One-against-all approach; Multi-class dataset classification
类别
资金
- Selcuk University
Generally. many classifier systems compel ill the classification Of multi-class problems. The aim of this study is to improve the classification accuracy in the case of multi-class classification problems. Ill this Study, We have proposed a novel hybrid classification system based on C4.5 decision tree classifier and one-against-all approach to classify the multi-class problems including dermatology, image segmentation. and lymphography datasets taken from UCI (University of California Irvine) machine learning database. To test the proposed method, we have used the classification accuracy, sensitivity-specificity analysis, and 10-fold cross validation. In this work, firstly C4.5 decision tree has been run for all the classes of dataset used and achieved 84.48%, 88.79%, and 80.11% classification accuracies for dermatology. image segmentation, and lymphography datasets using 10-fold cross validation, respectively. The proposed method based oil C4.5 decision tree classifier and one-against-all approach obtained 96.71%, 95.18%, and 87.95% for above datasets, respectively. These results show that the proposed method has produced very promising results in the classification of multi-class problems. This method call be used in many pattern recognition applications. In future, instead of C4.5 decision tree, other classification algorithms such as Bayesian learning, artificial immune system algorithms, artificial neural networks call be used. (c) 2007 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据