A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets

被引：0

作者：

Franco-Arcega, Anilu ^{[1
]}

Ariel Carrasco-Ochoa, J. ^{[1
]}

Sanchez-Diaz, Guillermo ^{[2
]}

Martinez-Trinidad, J. Fco ^{[1
]}

机构：

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

[2] Univ Guadalajara, Centro Univ Los Valles, Jalisco 46600, Mexico

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2008 | 2008年 / 5326卷

基金：

美国国家科学基金会; 美国国家航空航天局;

关键词：

Decision trees; supervised classification; large datasets;

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：

Several algorithms for induction of decision trees have been developed to solve problems with large datasets, however some of them have spatial and/or runtime problems using the whole training sample for building the tree and others do not take into account the whole training set. In this paper, we introduce a new algorithm for inducing decision trees for large numerical datasets, called IIMDT which builds the tree in all incremental way and therefore it, is not necesary to keep in main memory the whole training set. A comparison between IIMDT and ICE. an algorithm for inducing decision trees for large datasets, is shown.

引用

页码：282 / +

页数：3

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