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
收藏
页数:6
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