A classifier design based on combining multiple components by maximum entropy principle

Authors:
Akinori Fujino;Naonori Ueda;Kazumi Saito
Affiliations:
NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan;NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan;NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan
Venue:
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Year:
2005

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Abstract

Designing high performance classifiers for structured data consisting of multiple components is an important and challenging research issue in the field of machine learning. Although the main component of structured data plays an important role when designing classifiers, additional components may contain beneficial information for classification. This paper focuses on a probabilistic classifier design for multiclass classification based on the combination of main and additional components. Our formulation separately considers component generative models and constructs the classifier by combining these trained models based on the maximum entropy principle. We use naive Bayes models as the component generative models for text and link components so that we can apply our classifier design to document and web page classification problems. Our experimental results for three test collections confirmed that the proposed method effectively combined the main and additional components to improve classification performance.