An Improved Model for Gene Expression Data Analysis Using Combined Feature Reduction and ELM Approaches

On a worldwide scale, the second leading cause of death is cancer. To combat cancer, a variety of approaches have been proposed. It is strongly predicted that gene expression data would be used to help in the development of better cancer detection. On the other hand, gene expression data is dimensionally high, has a small sample size in comparison to the data dimension, and many genes are redundant and also noisy by nature. As a result, rather than using all genes to classify gene expression data, it would be wiser to use a subset of genes. Thus, in this research work, the proposed model is designed considering ranked-based filter sequence (FS) approaches for significant feature extraction and Extreme Learning Machine (ELM) for data classification. This analysis is carried out over four gene expression data. As FS approaches are highly successful for high dimension data, significant features are extracted using 4-stage feature reduction strategy applying RF approach. Thereafter the reduced data is applied for classification. ELM is a learning algorithm for a Single-Layer Feed-Forward Network (SLFN) that converges much faster than traditional methods and produces promising results. Thus, due to its superior approximation capability and generalization, ELM has equipped the interest of numerous researchers in recent years. In this analysis, considering different types of activation functions and considering a collection of network architecture, four numbers of ELM models are designed and used for classification purposes with four different activation functions. From all the experimental results it is found that IIELM (CM4) outperforms over all the model.