Ant Colony reduction with modified rules generation for rough classification model.
ABSTRACT In this paper we propose a rough classification modeling algorithm based on Ant Colony Optimization (ACO) reduction. We used ACO to compute the rough set reduct and later a modified rules generation method is employed to generate the classification rules. The rules generation algorithm used is the simplification of the Default Rules Generation Framework (DRGF) in order to fit with the ACO reduct. The performance of the proposed classifier is compared with the DRGF based classifier using genetic reduction. The experimental results show that the ACO-Rough performs better with higher classification accuracy and fewer number of rules.
- SourceAvailable from: Beata Walczak
Article: Rough sets theory[Show abstract] [Hide abstract]
ABSTRACT: The basic concepts of the rough set theory are introduced and adequately illustrated. An example of the rough set theory application to the QSAR classification problem is presented. Numerous earlier applications of rough set theory to the various scientific domains suggest that it also can be a useful tool for the analysis of inexact, uncertain, or vague chemical data.Chemometrics and Intelligent Laboratory Systems 04/1999; 47(1):1-16. · 2.38 Impact Factor
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ABSTRACT: Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset pre-processor with much success, but current methods are inadequate at finding minimal reductions, the smallest sets of features possible. To alleviate this difficulty, a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, has been developed recently and has been shown to be effective. However, this method is still not able to find the optimal subsets regularly. This paper proposes a new feature selection mechanism based on ant colony optimization in an attempt to combat this. The method is then applied to the problem of finding optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to complex systems monitoring and experimentally compared with the original fuzzy-rough method, an entropy-based feature selector, and a transformation-based reduction method, PCA. Comparisons with the use of a support vector classifier are also included.Fuzzy Sets and Systems 01/2005; · 1.88 Impact Factor
- J. Machine Learning Research Special Issue on Variable and Feature Selection. 01/2003; 3:1157 - 1182.