Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but also contain redundant information and noises. To cope with these difficulties, this paper introduces a fuzzy logic based pre-processing approach composed of two main steps. First, ...

this paper processes a combined method, based on vikor and data envelopment analysis (dea) to select the units with most efficiency. we utilize the vikor as compromise solution method. this research is a two-stage model designed to fully rank the alternatives, where each alternative has multiple inputs and outputs. the problem involves belief parameters in the solution procedure. first, the alt...

For complex and high-dimensional problems, data-driven identification of classifiers has to deal with structural issues like the selection of the relevant features and effective initial partition of the input domain. Therefore, the identification of fuzzy classifiers is a challenging topic. Decision-tree (DT) generation algorithms are effective in feature selection and extraction of crisp class...

banks are one of the most important financial sectors in order to the economic development of each country. certainly, efficiency scores and ranks of banks are significant and effective aspects towards future planning. sometimes the performance of banks must be measured in the presence of undesirable and vague factors. for these reasons in the current paper a procedure based on data envelopment...

In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the ...

Clustering is a popular data analysis and data mining technique. In this paper, a novel chaotic particle swarm fuzzy clustering (CPSFC) algorithm based on chaotic particle swarm (CPSO) and gradient method is proposed. Fuzzy clustering model optimization is challenging, in order to solve this problem, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC)...

Biometric techniques are gaining importance for personal authentication and identification as compared to the traditional authentication methods. Biometric templates are vulnerable to variety of attacks due to their inherent nature. When a person’s biometric is compromised his identity is lost. In contrast to password, biometric is not revocable. Therefore, providing security to the stored biom...

prediction, diagnosis, recovery and recurrence of the breast cancer among the patients are always one of the most important challenges for explorers and scientists. nowadays by using of the bioinformatics sciences, these challenges can be eliminated by using of the previous information of patients records. in this paper has been used adaptive nero fuzzy inference system and data mining techniqu...

Fuzzy decision tree induction algorithms require the fuzzy quantization of the input variables. This paper demonstrates that supervised fuzzy clustering combined with similarity-based rule-simplification algorithms is an effective tool to obtain the fuzzy quantization of the input variables, so the synergistic combination of supervised fuzzy clustering and fuzzy decision tree induction can be e...