A Spatial Feature Selection Method Based on Maximum Entropy Theory
基于最大熵原理的空间特征选择方法

Feature selection has an important application in the field of pattern recognition and data mining etc. However, in real world domains, if there are spatial data operated in the application, the performance of feature selection will be decreased because of without considering the characteristic of spatial data. In this paper, a feature selection method from the point of the characteristic of spatial data, named MEFS (maximum entropy feature selection), is proposed. Based on the theory of maximum entropy, MEFS uses mutual information and Z-test technologies, and takes two-step method to execute feature selection. The first step is predicate selection, and the second step is to choose relevant dataset corresponding to each predicate. At last, the experiments between feature selection algorithms MEFS and RELIEF, and between ID3 classification algorithm and classification algorithm based on MEFS are carried out. The experimental results show that the MEFS algorithm not only saves feature selection and classification time, but also improves the quality of classification.