Extreme Learning Machine for Two Category Data Classification

This paper experiments a recently developed, simple and efficient learning algorithm for Single hidden Layer Feed forward Neural networks (SLFNs) called Extreme Learning Machine (ELM) for two category data classification problems evaluated on the Stat log-Heart dataset. ELM randomly chooses hidden nodes and analytically determines the output weights of SLFNs. A detailed analysis of different activation functions with varying number of hidden neurons is carried out using Stat log-Heart dataset. The evaluation results indicate that ELM produces better classification accuracy with reduced training time. Its performance has been compared with other methods such as the Naive Bayes, AWAIS, C4.5, and Logistic Regression algorithms sited in the previous literature.