Abstract
This paper describes a way of designing a hybrid system for detecting the different stages of cervical cancer. Hybridisation includes the evolution of knowledge-based subnetwork modules with GAs using rough set theory and the ID3 algorithm. Crude subnetworks for each module are initially obtained via rough set theory and the ID3 algorithm. These subnetworks are then combined, and the final network is evolved using genetic algorithms. The evolution uses a restricted mutation operator which utilises the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conventional MLP. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for rule extraction.
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