Ensemble feature selection with adaptive weights

Most of the existing ensemble feature selection algorithms directly assign different feature selection algorithms the same weights or simply treat accuracy as weight and then vote for each feature. However, these research methods either ignored or did not fully consider the fitness of the feature selecttion algorithm to the data set. To deal with this challenge, we propose a new ensemble feature selection approach with adaptive weights. In detail, we use the softmax function to dynamically adjust the weight of the base feature selector according to its fitness to dataset in the k-round training process. We name it ensemble feature selection based on softmax function (EFS-BSF) algorithm. We demonstrate the superiority of the EFS-BSF approach over previous methods through mathematical analysis and experiments on multiple data sets.