The Research Of Feature Selection Based On Probability Density Approximation

Classification is the principal task of pattern recognition using the features of the patterns. Generally, a pattern can be correctly classified only when the one's features have enough classification information. In order to improve the accuracy of classification, the large features need to be collected, so that the original feature space is thousands or tens thousands dimensionalities. This will not only lead the dimensionality of the pattern to enlarge, but also lower the classification accuracy owing to the relativity and redundancy of the features. This is the so-called Curse of Dimensionality. So, in order to effectively analyze high dimensionality data, it is a pivotal step to reduce their dimensional members.The purpose of this paper is to explore a new feature selection way and propose a feature ranking method to reduce feature's dimensionalities. In this paper, the principle of reducing feature's dimensionalities is briefly introduced, and the principal ways of feature dimensionality reduction is reviewed. Probability density estimation is also introduced and non-parameter estimation and Parzen window probability density estimation is detailed. The emphasis of this paper is to deduce the feature selection theory based on probability density approximation, and to elaborate the principle and way of feature ranking using probability density approximation. In this paper, the Gaussian kernel Parzen estimation in high dimensional space is introduced and applied. It can embody more effectively the character of data. Aiming at feature selection, based on Parzen window probability density estimation and probability density approximation, a novel feature ranking approach is proposed. A simplified approach is introduced to deal with unsupervised data. At last, the algorithm proposed in this paper is realized by MATLAB, and many datasets is used to experiment. A lot of cross-validation and others experimental results demonstrate the validity, feasibility and advantage over others of our approach.