Dissimilarity based ensemble of extreme learning machine for gene expression data classification

Extreme learning machine (ELM) has salient features such as fast learning speed and excellent generalization performance. However, a single extreme learning machine is unstable in data classification. To overcome this drawback, more and more researchers consider using ensemble of ELMs. This paper proposes a method integrating voting-based extreme learning machines (V-ELMs) with dissimilarity (D-ELM). First, based on different dissimilarity measures, we remove a number of ELMs from the ensemble pool. Then, the remaining ELMs are grouped as an ensemble classifier by majority voting. Finally we use disagreement measure and double-fault measure to validate the D-ELM. The theoretical analysis and experimental results on gene expression data demonstrate that (1) the D-ELM can achieve better classification accuracy with less number of ELMs; (2) the double-fault measure based D-ELM (DF-D-ELM) performs better than disagreement measure based D-ELM (D-D-ELM).