A novel approach to model generation for heterogeneous data classification

Rong Jin, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Ensemble methods such as bagging and boosting have been successfully applied to classification problems. Two important issues associated with an ensemble approach are: how to generate models to construct an ensemble, and how to combine them for classification. In this paper, we focus on the problem of model generation for heterogeneous data classification. If we could partition heterogeneous data into a number of homogeneous partitions, we will likely generate reliable and accurate classification models over the homogeneous partitions. We examine different ways of forming homogeneous subsets and propose a novel method that allows a data point to be assigned multiple times in order to generate homogeneous partitions for ensemble learning. We present the details of the new algorithm and empirical studies over the UCI benchmark datasets and datasets of image classification, and show that the proposed approach is effective for heterogeneous data classification.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages746-751
Number of pages6
StatePublished - 2005
Event19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom
Duration: Jul 30 2005Aug 5 2005

Other

Other19th International Joint Conference on Artificial Intelligence, IJCAI 2005
CountryUnited Kingdom
CityEdinburgh
Period7/30/058/5/05

ASJC Scopus subject areas

  • Artificial Intelligence

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