Multi-knowledge extraction algorithm using Group Search Optimization for brain dataset analysis

Citation:
Zhu, Z., Z. Wang;, T. Li;, X. Wang, H. Liu, and A. E. Hassanien, "Multi-knowledge extraction algorithm using Group Search Optimization for brain dataset analysis", 2nd International Conference on Computing for Sustainable Global Development (INDIACom) 11-13 March, pp. 1891 – 1896, , India, 11 March, 2015.

Date Presented:

11 March

Related External Link

Knowledge is formed by a kind of mapping from the condition space to the decision space in rough set. This paper presents multi-knowledge extraction approaches with fuzzy population algorithms. The Group Search Optimization (GSO) and Particle Swarm Optimization (PSO) are compared. GSO not only has the rapid convergence speed, but also has low time complexity, especially for high dimensional datasets. We use the multi-knowledge extraction algorithm based on GSO to analyze the data of brain cognition datasets. The experimental results illustrate our algorithm is very promising to seek for the relationship between the active brain regions and stimuli.