Ordered incremental training for GA-based classifiers
Pattern Recognition Letters
Hierarchical Incremental Class Learning with Reduced Pattern Training
Neural Processing Letters
Computers and Industrial Engineering - Special issue: Computational intelligence and information technology applications to industrial engineering selected papers from the 33 rd ICC&IE
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
Computers and Industrial Engineering
Recursive hybrid decomposition with reduced pattern training
International Journal of Hybrid Intelligent Systems
Multi-objective GA rule extraction in a parallel framework
Proceedings of the 15th WSEAS international conference on Computers
Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set
International Journal of Applied Evolutionary Computation
Incremental Hyperplane Partitioning for Classification
International Journal of Applied Evolutionary Computation
Hi-index | 0.00 |
This paper proposes a class decomposition approach to improve the performance of GA-based classifier agents. This approach partitions a classification problem into several class modules in the output domain, and each module is responsible for solving a fraction of the original problem. These modules are trained in parallel and independently, and results obtained from them are integrated to form the final solution by resolving conflicts. Benchmark classification data sets are used to evaluate the proposed approaches. The experiment results show that class decomposition can help achieve higher classification rate with training time reduced.