Selective ensemble of doubly weighted fuzzy extreme learning machine for tumor classification

Malignant epithelial cell tumor also known as cancer is a deadly disease requiring a very costly and complex treatment. Early and accurate diagnosis of tumor plays an important role in reducing the mortality rate. With the rapid development of gene chip technology, gene expression data based tumor classification is helpful for accurate decision-making and has achieved great attention of researchers. Due to gene expression data having the properties of multi-class imbalance, high noise and high-dimensional small samples, in this paper, selective ensemble of doubly weighted fuzzy extreme learning machine (SEN-DWFELM) is presented for tumor classification. In view of good generalization performance of extreme learning machine (ELM), feature weighted fuzzy membership is embedded in ELM for eliminating classification error from noise samples. It considers the influence of feature importance on classification to acquire more accurate fuzzy membership. Simultaneously, by removing features with smaller weights it reduces the dimensionality of samples to improve training efficiency. Considering imbalanced learning, the weighted scheme is also introduced to enhance the effect of minority class samples on classification. Furthermore, doubly weighted fuzzy extreme learning machine (DWFELM) based selective ensemble algorithm is proposed to make classification performance more robust. Partial-based DWFELMs are selected using binary version of an improved whale optimization algorithm, and the selected base DWFELMs are integrated by majority voting. Finally, the proposed SEN-DWFELM is compared with conventional ensemble methods and variants of SEN-DWFELM on various gene expression data. Experimental results show that SEN-DWFELM remarkably outperforms other competitors in accordance with classification performance and can effectively deal with tumor diagnosis problems.