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Neural Computing and Applications

, Volume 31, Issue 4, pp 1173–1187 | Cite as

A discriminative model selection approach and its application to text classification

  • Lungan Zhang
  • Liangxiao JiangEmail author
  • Chaoqun Li
Original Article
  • 150 Downloads

Abstract

Classification is one of the fundamental problems in data mining, in which a classification algorithm attempts to construct a classifier from a given set of training instances with class labels. It is well known that some classification algorithms perform very well on some domains, and poorly on others. For example, NB performs well on some domains, and poorly on others that involve correlated features. C4.5, on the other hand, typically works better than NB on such domains. To integrate their advantages and avoid their disadvantages, many model hybrid approaches, such as model insertion and model combination, are proposed. In this paper, we focus on a novel view and propose a discriminative model selection approach, called discriminative model selection (DMS). DMS discriminatively chooses different single models for different test instances and retains the interpretability of single models. Empirical studies on a collection of 36 classification problems from the University of California at Irvine repository show that our discriminative model selection approach outperforms single models, model insertion approaches and model combination approaches. Besides, we apply the proposed discriminative model selection approach to some state-of-the-art naive Bayes text classifiers and also improve their performance.

Keywords

Naive Bayes C4.5 Discriminative model selection Text classification 

Notes

Acknowledgements

The work was partially supported by the National Natural Science Foundation of China (61203287), the Program for New Century Excellent Talents in University (NCET-12-0953), the Chenguang Program of Science and Technology of Wuhan (2015070404010202), and the Open Research Project of Hubei Key Laboratory of Intelligent Geo-Information Processing (KLIGIP201601).

Compliance with ethical standards

Conflict of interest

We confirm that there is no conflict of interest in the submission, and the manuscript has been approved by all authors for publication.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratory of Intelligent Geo-Information ProcessingChina University of GeosciencesWuhanChina
  3. 3.Department of MathematicsChina University of GeosciencesWuhanChina

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