Ensemble based fuzzy weighted extreme learning machine for gene expression classification

Multi-class imbalance is one of the challenging problems in many real-world applications, from medical diagnosis to intrusion detection, etc. Existing methods for gene expression classification usually assume relatively balanced class distribution. However, the assumption is invalid for imbalanced data learning. This paper presents an effective method named EN-FWELM for class imbalance learning. First, based on a fast classifier extreme learning machine (ELM), fuzzy membership of sample is proposed in order to eliminate classification error coming from noise and outlier samples, and balance factor is introduced in combination with sample distribution and sample number associated with class to alleviate the bias against performance caused by imbalanced data. Furthermore, ensemble of ELMs is used for making classification performance more stable and accurate. A number of base ELMs are removed based on dissimilarity measure, and the remaining base ELMs are integrated by majority voting. Finally, experimental results on various gene expression classification and real-world classification demonstrate that the proposed EN-FWELM remarkably outperforms other approaches in the literature.