Gene selection for designing optimal fuzzy rule base classifier by estimating missing value

DNA microarray technology, a high throughput technology evaluates the expression of thousands of genes simultaneously under different experimental conditions. Analysis of the gene expression data reveals that not all but few important genes are responsible for the diseases. However, the DNA microarray data set usually contain multiple missing value and therefore, selection of important genes using the incomplete data set may be erroneous, resulting misclassification in disease prediction. In the paper we propose an integrated framework, which first imputes the missing value and then in order to achieve maximum accuracy in classifying the patients a classifier has been designed to select the genes using the complete microarray data set.Here functionally similar genes are employed to estimate the missing value unlike the existing gene expression value based distance similarity measure. However, the functionally similar genes may differ in their protein production capacity and so the degree of similarity between the genes varies from gene to gene. The problem has been dealt by proposing a novel method to impute the missing value using the concept of fuzzy similarity. After imputing the missing value, the continuous gene expression matrix is discretized using fuzzy sets to distinguish the activation levels of different genes. The proposed fuzzy importance factor (FIf) of each gene represents its activation level or protein production capacity both in the disease and normal class. The importance of each gene is evaluated while optimizing the number of rules in the fuzzy classifier depending on the FIf. The methodology we propose has been demonstrated using nine different cancer data sets and compared with the state of the art methods. Analysis of experimental results reveals that the proposed framework able to classify the diseased and normal patients with improved accuracy.