UTHM Institutional Repository

The effect of data pre-processing on optimized training of artificial neural networks

Mohd Nawi, Nazri and Atomia, Walid Hasen and Rehman, Mohammad Zubair (2013) The effect of data pre-processing on optimized training of artificial neural networks. In: 4th International Conference on Electrical Engineering and Informatics (ICEEI 2013), 24-25 June 2013, Universiti Kebangsaan Malaysia.

[img]
Preview
PDF
818.pdf

Download (309kB)

Abstract

Recently, the popularity of artificial neural networks (ANN) is increasing since its capacity to model very complex problems in the area of Machine Learning, Data Mining and Pattern Recognition. Improving training efficacy of ANN based algorithm is a dynamic area of research and several papers have been reviewed in the literature. The performance of Multi-layer Perceptrons (MLP) trained with Back Propagation Artificial Neural Network (BP-ANN) method is highly influenced by the size of the datasets and the data-preprocessing techniques used. This work analyses the benefits of using pre-processing datasets using different techniques in-order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal Scaling Normalization preprocessing techniques were evaluated. The simulation results show that the computational efficiency of ANN training process is highly enhanced when coupled with different preprocessing techniques.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: artificial neural networks; back propagation; gradient descent; gain value; pre-processing data
Subjects: Q Science > QA Mathematics > QA75 Calculating machines > QA75.5 Electronic computers. Computer science
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 28 Aug 2013 01:52
Last Modified: 28 Aug 2013 01:52
URI: http://eprints.uthm.edu.my/id/eprint/4098
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year