A Comparative Study of Different Machine Learning Algorithms on Gene Expression Profile Classification

Abstract

Many machine learning algorithms have been used to classify gene expression profiles in recently years. In order to furtherly study the performances of different machine learning algorithms on gene expression profiles classification, this paper compare the classification accuracy, run time and stability the of different machine learning algorithms including SVM, Decision tree, PNN, k-Nearest Neighbor, Bayesian and ELM on benchmark gene expression profile datasets. It provides a basis for scientific use of machine learning algorithms on gene expression profiles classification.