期刊
KNOWLEDGE-BASED SYSTEMS
卷 23, 期 6, 页码 605-614出版社
ELSEVIER
DOI: 10.1016/j.knosys.2010.04.007
关键词
Associative classification rules; Data mining; Genetic algorithm; Numerical data
资金
- National Science Council of Taiwan [NSC-096-2917-1-008-110]
Associative classifiers are a classification system based on associative classification rules Although associative classification is more accurate than a traditional classification approach, it cannot handle numerical data and its relationships Therefore, an ongoing research problem is how to build associative classifiers from numerical data In this work, we focus on stock trading data with many numerical technical indicators, and the classification problem is finding sell and buy signals from the technical indicators This study proposes a GA-based algorithm used to build an associative classifier that can discover trading rules from these numerical indicators The experiment results show that the proposed approach is an effective classification technique with high prediction accuracy and is highly competitive when compared with the data distribution method (C) 2010 Elsevier B.V. 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