High-dimensional supervised feature selection via optimized kernel mutual information

Feature selection is very important for pattern recognition to reduce the dimensions of data and to improve the efficiency of learning algorithms. Recent research on new approaches has focused mostly on improving accuracy and reducing computing time. This paper presents a flexible feature-selection method based on an optimized kernel mutual information (OKMI) approach. Mutual information (MI) has been applied successfully in decision trees to rank variables; its aim is to connect class labels with the distribution of experimental data. The use of MI removes irrelevant features and decreases redundant features. However, MI is usually less robust when the data distribution is not centralized. To overcome this problem, we propose to use the OKMI approach, which combines MI and a kernel function. This approach may be used for feature selection with nonlinear models by defining kernels for feature vectors and class-label vectors. By optimizing the objection equations, we develop a new feature-selection algorithm that combines both MI and kernel learning, we discuss the relationship among various kernel-selection methods. Experiments were conducted to compare the new technique applied to various data sets with other methods, and in each case the OKMI approach performs better than the other methods in terms of feature-classification accuracy and computing time. OKMI method solves the problem of computation complexity in the probability of distribution, and avoids this problem by finding the optimal features at very low computational cost. As a result, the OKMI method with the proposed algorithm is effective and robust over a wide range of real applications on expert systems.