Feature selection based on extreme learning machine

Feature selection (FE) is a crucial pre-processing in pattern classification. FE addresses the problem of finding the most compact and informative subset of initial feature set to improve the performance of pattern classification system or to reduce the storage requirement. Recently, Yang et al. proposed a wrapper-based feature selection method for multilayer perceptron (MLP) neural networks. The learning speed of the algorithm is very slow, especially for large database, due to iteratively tuning the weight parameters of the networks with back propagation algorithm. In order to deal with this problem, based on extreme learning machine (ELM), we propose a feature selection algorithm which uses a feature ranking criterion to measure the significance of a feature by computing the aggregate difference of the outputs of the probabilistic SLFN with and without the feature. The SLFN is trained with ELM which randomly chooses the weights of hidden layer and analytically determines the weights of output layer. We compared the proposed algorithm with the Yang's work and other three feature selection algorithms. The experimental results show that our proposed method is effective and efficient.