Title :
Extreme Learning Machine for two category data classification
Author :
Subbulakshmi, C.V. ; Deepa, S.N. ; Malathi, N.
Author_Institution :
EEE Dept., Avinashilingam Univ. for Women, Coimbatore, India
Abstract :
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 Naïve Bayes, AWAIS, C4.5, and Logistic Regression algorithms sited in the previous literature.
Keywords :
data mining; feedforward neural nets; learning (artificial intelligence); pattern classification; ELM; SLFN; Stat log-Heart dataset; data mining; efficient learning algorithm; extreme learning machine; hidden neurons; single hidden layer feed forward neural networks; stat log-heart dataset; two category data classification; Accuracy; Heart; Mercury (metals); Standards; Classification; Extreme Learning Machine (ELM); Single hidden Layer Feed forward Neural network (SLFN);
Conference_Titel :
Advanced Communication Control and Computing Technologies (ICACCCT), 2012 IEEE International Conference on
Conference_Location :
Ramanathapuram
Print_ISBN :
978-1-4673-2045-0
DOI :
10.1109/ICACCCT.2012.6320822