FEATURE SELECTION USING C4.5 ALGORITHM FOR ELECTRICITY PRICE PREDICTION

被引：0

作者：

Qian, Hehui ^{[1
]}

Qiu, Zhiwei ^{[1
]}

机构：

[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China

来源：

PROCEEDINGS OF 2014 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1 | 2014年

基金：

中国国家自然科学基金;

关键词：

Decision tree; C4.5; Electricity price forecasting; Feature selection; DECISION TREES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The electricity price foresting is important in our daily life. It does not only benefit to the customers but also the providers since the pressure of the load station in the rush hours can be reduced. As there are a lot of history information can be adopted, one of the problems for the electricity price foresting is how to select the useful features in order to increase the accuracy of the foresting and also reduce the time complexity. This paper we apply the decision tree c4.5 to select the relevant features for electricity price foresting. We show the performance of C4.5 is better than the ID3 in terms of accuracy experientially.

引用

页码：175 / 180

页数：6

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