Abstract
One of the important application for gene expression profiling technology in medical field is to support clinical decision in the form of diagnosis of disease and the prediction of clinical outcomes in response to treatment. The disease prediction and diagnosis become popular in the machine learning field and gene expression data classification problem has attracted considerable research interests in recent years. The challenges posed in gene expression data classification are the limited size of samples and the high dimensionality of the sample. Data gravitation based classification (DGC) model is a novel classification algorithm which performs well in many classification problems. Also, there is an important character of DGC to deal with gene expression data classification problem, feature weighing procedure which measures the importance of a feature by weighting them. In this study, we design a classifier based on the basic DGC model namely k-DGC for the gene selection and classification of gene expression data. We use ANOVA as a filter which can quickly reduce the number of genes and then apply our proposed k-DGC model based on the concept of K-Nearest Neighbor (KNN) and use improved Simplified Swarm Optimization algorithm (iSSO) to optimize the feature weight. Leave one out cross validation (LOOCV) served as an evaluator of the k-DGC model. We compared our method k-DGC with previous research by running ten gene expression datasets from GEMS. Experimental results show that our method is effective for gene expression data classification problems.