An Effective Cancer Classification Using Machine Learning Algorithms

In this paper, the recently developed Extreme Learning Machine (ELM) is used for direct multicategory classification problems in the cancer diagnosis area. It uses Microarray gene expression cancer diagnosis for directing multicategory classification problems in the cancer diagnosis area. The common problems faced by iterative learning methods like local minima improper learning rate and over fitting are avoided by ELM. ELM completes the training at a faster rate. We have evaluated the multicategory classification performance of ELM on three benchmark microarray data sets for cancer diagnosis, namely, the Lymphoma data set. The results indicate that ELM produces comparatively better classification accuracies with reduced training time. The implementation complexity of ELM is very less compared to artificial neural networks methods like conventional back-propagation ANN, Linder's SANN and Support vector machine.