Research And Implementation Of Fuzzy Entropy-based Feature Selection Method

Feature selection process plays an important role in pattern classification, whether or not the correct choice of features directly related to the classification accuracy rate, so feature selection method is a direct impact on system performance and quality. However, the current existence of the majority of feature selection methods are easily trapped into local optimization problems, in order to be able to quickly and accurately find the smallest feature subset in order to better achieve the purpose of classification, feature selection methods is explored and tried bold in this paper.Ant colony optimization algorithm, as a swarm intelligence algorithm, with a swarm intelligence algorithm can resolve most of the advantages of global optimization algorithms. Ant colony optimization algorithm is a combinatorial optimization problem solving the new common heuristic method, ant colony optimization algorithm with positive feedback, distributed computing and self-organizational characteristics; it is a greedy heuristic search method. In practical applications, we found that ant colony optimization algorithm for the shortcomings of a longer search time, so choose the correct and effective pheromone and constraints become important. Ant colony optimization algorithm can be divided into two basic stages: stage adaptation stage and collaboration. In the adaptation phase, all candidate solutions according to the information accumulated by constantly adjusting its structure. In the collaboration phase, each candidate solution in the continuous exchange of information, resulting in better performance of the solution.Entropy of a probability distribution describing the degree of uncertainty. Transplanted to the entropy concept of fuzzy sets theory, can be fuzzy entropy. Fuzzy entropy describes a degree of ambiguity of fuzzy sets. A fuzzy set of the vaguer, and its fuzzy entropy, the greater and vice versa. Fuzzy entropy describes the probability distribution of the degree of uncertainty, the greater the uncertainty of a collection, its distribution more uniform, the greater the amount of information. In this article, fuzzy entropy as a pheromone used in ant colony optimization algorithm, the ant colony quickly and accurately to help find important feature subset. First, the entire data set as an ant colony optimization algorithm for the input, and then randomly select a set of features as the initial feature subset, and then let the ant colony movement in accordance with the rules to change the various characteristics of pheromones, finally won the goal of feature subset.