Please use this identifier to cite or link to this item: https://doi.org/10.1016/0950-7051(95)01030-0
Title: Dimensionality reduction via discretization
Authors: Liu, H. 
Setiono, R. 
Keywords: Dimensionality reduction
Discretization
Knowledge discovery
Issue Date: Feb-1996
Source: Liu, H., Setiono, R. (1996-02). Dimensionality reduction via discretization. Knowledge-Based Systems 9 (1) : 67-72. ScholarBank@NUS Repository. https://doi.org/10.1016/0950-7051(95)01030-0
Abstract: The existence of numeric data and large numbers of records in a database present a challenging task in terms of explicit concepts extraction from the raw data. The paper introduces a method that reduces data vertically and horizontally, keeps the discriminating power of the original data, and paves the way for extracting concepts. The method is based on discretization (vertical reduction) and feature selection (horizontal reduction). The experimental results show that (a) the data can be effectively reduced by the proposed method; (b) the predictive accuracy of a classifier (C4.5) can be improved after data and dimensionality reduction; and (c) the classification rules learned are simpler.
Source Title: Knowledge-Based Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/99243
ISSN: 09507051
DOI: 10.1016/0950-7051(95)01030-0
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

27
checked on Jan 17, 2018

WEB OF SCIENCETM
Citations

19
checked on Jan 17, 2018

Page view(s)

25
checked on Jan 12, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.