Instance selection based on sample entropy for efficient data classification with ELM
Xizhao Wang, Qing Miao, Mengyao Zhai, Junhai Zhai · 2012
Instance selection also named sample selection is an important preprocessing step for pattern classification. Almost all of the existing instance selection methods are developed for specific classifiers, such as nearest neighbor (NN) classifier, support vector machine (SVM) classifier. Few of them are designed for single hidden layer feed-forward neural networks (SLFNs) classifier. Based on sample entropy, this paper presents an instance selection method for efficient data classification with extreme learning machine (ELM), which is used to train a SLFN. The proposed method is compared with four state-of-the-art approaches by a series of experiments. The experimental results show that the proposed method can provide similar generalization performance but lower computation time complexity.