An effective discretization based on class-attribute coherence maximization
文献类型：期刊论文
| 作者 | Li, Min; Deng, ShaoBo; Feng, Shengzhong; Fan, Jianping |
| 刊名 | Pattern Recognition Letters
 |
| 出版日期 | 2011 |
| 卷号 | 32期号:15页码:1962-1973 |
| 英文摘要 | Discretization of continuous data is one of the important pre-processing tasks in data mining and knowledge discovery. Generally speaking, discretization can lead to improved predictive accuracy of induction algorithms, and the obtained rules are normally shorter and more understandable. In this paper, we present the Class-Attribute Coherence Maximization (CACM) algorithm and the Efficient-CACM algorithm. We have compared the performance of our algorithms with the most relevant discretization algorithm, Fast Class-Attribute Interdependence Maximization (Fast-CAIM) discertization algorithm (Kurgan and Cios, 2003). Empirical evaluation of our algorithms and Fast-CAIM on 12 well-known datasets shows that ours generate the superior discretization scheme, which can significantly improve the classification performance of C4.5 and RBF-SVM classifier. As to the execution time of discretization, ours also prove faster than Fast-CAIM algorithm, with the Efficient-CACM algorithm having the shortest execution time. (C) 2011 Elsevier B.V. All rights reserved. |
| 收录类别 | SCI |
| 原文出处 | http://www.sciencedirect.com/science/article/pii/S0167865511002595 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/3553]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | Pattern Recognition Letters |
| 推荐引用方式 GB/T 7714 | Li, Min,Deng, ShaoBo,Feng, Shengzhong,et al. An effective discretization based on class-attribute coherence maximization[J]. Pattern Recognition Letters,2011,32(15):1962-1973. |
| APA | Li, Min,Deng, ShaoBo,Feng, Shengzhong,&Fan, Jianping.(2011).An effective discretization based on class-attribute coherence maximization.Pattern Recognition Letters,32(15),1962-1973. |
| MLA | Li, Min,et al."An effective discretization based on class-attribute coherence maximization".Pattern Recognition Letters 32.15(2011):1962-1973. |
入库方式： OAI收割
来源：深圳先进技术研究院
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