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-ELM) with dissimilarity (D-ELM). First, based on different dissimilarity measures, we remove 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).