Universiti Teknologi Malaysia Institutional Repository

Forecasting small data set using hybrid cooperative feature selection

Sallehuddin, Roselina and Shamsuddin, Siti Mariyam and Mohd. Hashim, Siti Zaiton (2010) Forecasting small data set using hybrid cooperative feature selection. In: International Journal of Simulation : System, Science and Technology, 24 - 26 March 2010, Cambridge University (Emmanuel College), England.

Full text not available from this repository.

Abstract

The aim of this paper is to propose the cooperative feature selection (CFS) to automatically select the critical factors that affect the performance of the forecasting performance of a small time series data. CFS sequentially combines grey relational analysis (GRA) and artificial neural network (ANN), which represents wrapper and filter method respectively. To test the efficiency of the proposed feature selection, it is employed to predict the total earnings of Malaysia Natural rubber based products. Results from the study shows that the proposed cooperative feature selections can increase the accuracy performance and learning time. Additionally, it also can work well in small data set and automatically choose the critical factor without human assistance.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Grey relational analysis, artificial neural network, cooperative feature selection, forecasting, total export earnings
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:23991
Deposited By: Liza Porijo
Deposited On:24 Sep 2012 03:32
Last Modified:24 Sep 2012 03:32

Repository Staff Only: item control page