Interactive Knowledge Discovery Using Agent based Cultural Algorithm for Thyroid Classification

M. Deepika and Dr.K. Kalaiselvi

The innovation made in evolutionary process resolves the issues using social intelligence, named cultural algorithm. It makes use of wider knowledge sources to make better decision systems. The role of classification rules are used for developing actionable knowledge to users. In this paper, we have focused on adding intelligent agent to the cultural algorithms in order to achieve better thyroid classification systems. Initially, the population has been defined for its relevant features. Here, we have added two sorts of agents, namely, grid and phenotypic knowledge agents in the belief space. Once the population with its relevant features is defined, then the agents do the process by updating the local and global extreme a. By doing so, the systems help to locally update the relevant features and its optimal solution. Experimental analysis has shown the efficiency of the optimal solutions via accuracy with better execution time.

Volume 11 | 04-Special Issue

Pages: 482-488