Paper Info
Title
Forecasting Small Data Set Using Hybrid Cooperative Feature Selection
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.
Year
DOI
Venue
2010
10.1109/UKSIM.2010.23
Computer Modelling and Simulation
Keywords
Field
DocType
small data,cfs sequentially,forecasting small data set,hybrid cooperative feature selection,proposed cooperative feature selection,malaysia natural rubber,cooperative feature selection,critical factor,accuracy performance,small time series data,forecasting performance,proposed feature selection,neural nets,feature selection,forecasting,computer simulation,testing,computational modeling,feature extraction,artificial neural network,artificial neural networks,data handling,natural rubber,time series data,data models,predictive models,accuracy,economic forecasting,rubber,time series,grey relational analysis
Data modeling,Data mining,Time series,Small data,Feature selection,Computer science,Grey relational analysis,Feature extraction,Artificial intelligence,Artificial neural network,Group method of data handling,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-6614-6
1
0.43
References 
Authors
3
3
Name
Order
Citations
PageRank
Roselina Sallehuddin1327.31
Siti Mariyam Shamsuddin239841.80
Siti Zaiton Mohd Hashim329526.44