Identification of Erythemato-Squamous Skin Diseases Using Support Vector Machines and Extreme Learning Machines: A Comparative Study towards Effective Diagnosis - Olusanya Olatunji, Sunday; Arif, Hossain (Transactions on Engineering and Computing Sciences, 2015)
Extreme Learning Machine (ELM) is a recently introduced learning algorithm for single hidden-layer feedforward neural network. Compared with classical learning algorithms in neural network, e.g. Back Propagation, ELM can achieve better performance with much shorter learning time. In the existing literature its better performance and comparison with Support Vector Machine (SVM), over regression and general classification problems, have caught the attention of many researchers. In this work, a comparison between ELM and SVM on identification of Erythemato-Squamous skin diseases is investigated. Detailed comparative studies were carried out through adequate experimentation. Experimental results indicated that ELM outperformed SVM. The effect of varying the size of training and testing sets on the performance of classifiers was also investigated in this study. The two techniques compared proved to be viable tools in this germane field of medical diagnosis.