Two-stage feature selection algorithm based onmutual information and genetic algorithm
基于互信息和遗传算法的两阶段特征选择方法

To get better feature subset in the feature selection process, this paper proposed a new two-stage feature selection algorithm based on normalized mutual information and genetic algorithm. First it ranked features by normalized mutual information. Then to provide the genetic algorithm with better starting point it used the front ranking features to initialize the population, thus got better feature subset after only a few evolution times. The test results on benchmark datasets show the effectiveness of the algorithm, in terms of dimensionality reduction and classification performance.