Multivariate Decision Trees Using Different Splitting Attribute Subsets for Large Datasets

被引：0

作者：

Franco-Arcega, Anilu ^{[1
]}

Ariel Carrasco-Ochoa, Jose ^{[1
]}

Sanchez-Diaz, Guillermo ^{[2
]}

Fco Martinez-Trinidad, Jose ^{[1
]}

机构：

[1] Natl Inst Astrophys Opt & Elect, Dept Comp Sci, Luis Enrique Erro 1, Puebla 72840, Mexico

[2] Univ Guadalajara, CUValles, Computat Sci & Technol Dept, Guadalajara 46600, Jalisco, Mexico

来源：

ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2010年 / 6085卷

关键词：

Decision tree(DT); large datasets; supervised classification; INDUCTION;

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：

In this paper, we introduce an incremental induction of multivariate decision tree algorithm, called IIMDTS, winch allows choosing a different splitting attribute subset in each internal node of the decision tree and it processes large datasets. IIMDTS uses all instances of the training set for building the decision tree without storing the whole training set in memory. Experimental results show that our algorithm is faster than three of the most recent algorithms for building decision trees for large datasets.

引用

页码：370 / +

页数：2

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