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Designing efficient discriminant functions for multi-category classification using evolutionary methods
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DOI：10.1016/j.neucom.2015.08.093.png)
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Abstract
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In this paper, we propose two approaches to obtain accurate classifiers for dealing with multi-category classification problem. Our work is based on one-vs-all strategy where we try to decrease conflicting situations. In the first phase of both approaches we employ Genetic Programming to find populations of the best discriminant functions (one population for each class). In addition to traditional function set, like {+, -, *, /}, we utilize other special functions in our binary trees. We also use both negative and positive constants in the terminal nodes of the trees. In the second phase, we employ Ant Colony in our first approach, called GP-Ant, and Genetic Algorithm in the second one, called GP-GA, to find the best combination of discriminant functions found in the previous phase. We also provide a special modification box to modify the decision of our final integrated classifiers, when conflicting situations happen. To cope with conflicting situations, we also utilize an appropriate fitness function in the second phase. We compare our works with both state of the art and basic multi-category classification methods on eight well-known publicly available data sets. Our experimental results show that our methods are statistically significantly better than all the Other classification methods used. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Multi-category classification
Ant colony system
Genetic programming
Discriminant functions
One-vs-all strategy
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