Multicategory classification using an Extreme Learning Machine for microarray gene expression cancer diagnosis

Abstract

This paper deals with the advanced and developed methodology know for cancer multi classification using an Extreme Learning Machine (ELM) for microarray gene expression cancer diagnosis, this used for directing multicategory classification problems in the cancer diagnosis area. ELM avoids problems like local minima; improper learning rate and over fitting commonly faced by iterative learning methods and completes the training very fast. We have evaluated the multicategory0 classification performance of ELM on benchmark microarray data sets for cancer diagnosis, namely, the Lymphoma data set. The results indicate that ELM produces comparable or better classification accuracies with reduced training time and implementation complexity compared to artificial neural networks methods like conventional back-propagation ANN, Linder's SANN, and Support Vector Machine.