On the suitability of Extreme Learning Machine for gene classification using feature selection

This paper studies the suitability of Extreme Learning Machines (ELM) for resolving bioinformatic and biomedical classification problems. In order to test their overall performance, an experimental study is presented based on five gene microarray datasets found in bioinformatic and biomedical domains. The Fast Correlation-Based Filter (FCBF) was applied in order to identify salient expression genes among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. The results confirm that the ELM classifier is a promising candidate for improving Accuracy and Minimum Sensitivity.