Multi-category bioinformatics dataset classification using extreme learning machine

Authors:
Tarek Helmy;Zeehasham Rasheed
Affiliations:
Information and Computer Science Department, College of Computer Science and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia;Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

This paper presents recently introduced learning algorithm called Extreme Learning Machine (ELM) for Single-hidden Layer Feed-forward Neural-networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. The 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 multi-category classification performance of ELM on five different data sets related to bioinformatics namely, the Breast Cancer Wisconsin data set, the Pima Diabetes data set, the Heart-Statlog data set, the Hepatitis data set and the Hypothyroid data set. A detailed analysis of different activation functions with varying number of neurons is also carried out which concludes that Algebraic Sigmoid function outperforms all other activation functions on these data sets. The evaluation results indicate that ELM produces better classification accuracy with reduced training time and implementation complexity compared to earlier implemented models.