Cancer data classification using a fuzzy classifier based on bio-inspired algorithms

Build classifier based on fuzzy rules for high-dimensional data sets, such as genetic data, are faced with great difficulties. An effective approach to this problem using feature selection techniques and dimension reduction methods. Hence, in this paper, using five different feature selection methods, size of data is reduced and the based on accuracy of the support vector machines classifier to this data a five dimensional feature vector extracted then using frog leaping algorithm and genetic algorithm, With the aim of minimizing the number of rules and optimize the parameters of its a set of fuzzy rules for data classification are extracted. The proposed method was tested on five gene expression datasets. The experiments results show that the proposed method achieves higher accuracy than existing.