Integrating Global and Local Boosting

被引：0

作者：

Lipitakis, Anastasia-Dimitra ^{[1
]}

Antzoulatos, Gerasimos S. ^{[1
]}

Kotsiantis, Sotiris ^{[1
]}

Vrahatis, Michael N. ^{[1
]}

机构：

[1] Univ Patras, Dept Math, GR-26110 Patras, Greece

来源：

2015 6TH INTERNATIONAL CONFERENCE ON INFORMATION, INTELLIGENCE, SYSTEMS AND APPLICATIONS (IISA) | 2015年

关键词：

global boosting; local boosting; ensemble of classifiers; machine learning; classification; heterogeneous databases; inductive learning system; RANDOM SUBSPACE METHOD; CLASSIFIER;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Several data analysis problems require investigations of relationships between attributes in related heterogeneous databases, where different prediction models can be more appropriate for different regions. A new technique of integrating global and local boosting is proposed. A comparison with other well known and widely used combining methods on standard benchmark datasets has shown that the proposed technique leads to more accurate results.

引用

页数：6

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