An Improved Kernel Credal Classification Algorithm Based on Regularized Mahalanobis Distance: Application to Microarray Data Analysis

Abstract

Within the kernel methods, an improved kernel credal classification algorithm (KCCR) has been proposed. The KCCR algorithm uses the Euclidean distance in the kernel function. In this article, we propose to replace the Euclidean distance in the kernel with a regularized Mahalanobis metric. The Mahalanobis distance takes into account the dispersion of the data and the correlation between the variables. It differs from Euclidean distance in that it considers the variance and correlation of the dataset. The robustness of the method is tested using synthetic data and a benchmark database. Finally, a set of DNA microarray data from Leukemia dataset was used to show the performance of our method on real-world application.