Enhancing ELM by Markov Boundary based feature selection

ELM, as an efficient classification technology, has been used in many popular application domains. However, ELM has weak generalization performance when the data set is small with respect to its feature space. In this paper, an enhanced ELM algorithm based on representative features is proposed to address the problem. At first, the method automatically generates some discrete intervals for every continuous feature. Then, it removes the irrelevant features by a method considering the feature interaction and reduces the weakly relevant features by a mutual information based method. Further, the reduction of redundancy features is conducted. Instead of constructing a large Bayesian network using all features, we just select the features of high relevance with the object node by an improved Markov Boundary identifying algorithm. Finally, we obtain the enhanced ELM classifier by training ELM using the extracted representative features and a genetic algorithm based weight assignment mechanism. The experiments conducted on real and synthetic small sample data sets demonstrate that the enhanced ELM classifier based on representative features outperforms the other methods used in our comparison study in terms of both efficiency and effectiveness.