Conference proceeding
Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications
Proceedings 2001 IEEE International Conference on Data Mining, pp 233-240
2001
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The article presents a novel approach to constructing a good ensemble of classifiers using rough set theory and database operations. Ensembles of classifiers are formulated precisely within the framework of rough set theory and constructed very efficiently by using set-oriented database operations. Our method first computes a set of reducts which include all the indispensable attributes required for the decision categories. For each reduct, a reduct table is generated by removing those attributes which are not in the reduct. Next, a novel rule induction algorithm is used to compute the maximal generalized rules for each reduct table and a set of reduct classifiers is formed based on the corresponding reducts. The distinctive features of our method as compared to other methods of constructing ensembles of classifiers are: (1) presents a theoretical model to explain the mechanism of constructing ensemble of classifiers; (2) each reduct is a minimum subset of attributes and has the same classification ability as the entire attributes; (3) each reduct classifier constructed from the corresponding reduct has a minimal set of classification rules, and is as accurate and complete as possible and at the same time as diverse as possible from the other classifiers; (4) the test indicates that the number of classifiers used to improve the accuracy is much less than other methods.
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Details
- Title
- Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications
- Creators
- Xiaohua Hu - Vigilance Inc., Sunnyvale, CA, USA
- Publication Details
- Proceedings 2001 IEEE International Conference on Data Mining, pp 233-240
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000173158200030
- Other Identifier
- 991019189081904721
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- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic