Please use this identifier to cite or link to this item:
|Title:||Dimensionality reduction via discretization|
|Authors:||Liu, H. |
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 17, 2018
WEB OF SCIENCETM
checked on Oct 10, 2018
checked on Oct 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.