Accession number:20094812515715
Title: Data attribute reduction using binary conversion Authors: Chang, Fengming M. (1)
Author affiliation:(1) Department of Information Science and Applications, Asia University Wufeng, Taichung 41354, Taiwan Corresponding author:Chang, F. M.
Source title: WSEAS Transactions on Computers Abbreviated source title:WSEAS Trans. Comput.
Volume:8 Issue:7
Issue date:2009
Publication year:2009 Pages:1144-1154 Language:English ISSN:11092750
Document type:Journal article (JA)
Publisher:World Scientific and Engineering Academy and Society, Ag.
Ioannou Theologou 17-23, Zographou, Athens, 15773, Greece
Abstract:While learning with data having large number of attribute, a system is easy to freeze or shut down or run for a long time.
Therefore, the proposed Binary Conversion (BC) is a novel method to solve this kind of large attribute problem in machine learning. The purpose of BC is to reduce data dimensions by a binary conversion process. All the attributes are reserved but combined into few numbers of new attributes instead of that some attributes are removed. To prevent the information loss problem during the conversion, each binary type data value occupies its own digital position in BC. In addition, 4 data sets: nbuses, ACLP, MONK3, and Buseskod data are used in this study to test and compare the
learning accuracies and learning time. The results indicate that the proposed BC can keep about the same level of accuracy but
increase the learning efficiency.
Number of references:32 Main heading:Education
Controlled terms: Robot learning
Uncontrolled terms: Binary conversion - Data attributes - Data
dimensions - Data sets - Data values - Information loss - Learning accuracy - Learning efficiency - Learning time - Machine-learning - Neuro-Fuzzy - Novel methods - Shut down
Classification code:731.5 Robotics - 901.2 Education Database:Compendex
Compilation and indexing terms, Copyright 2009 Elsevier Inc.
