Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/41404
Title: Feature selection algorithms for very high dimensional data and mixed data
Authors: Tang, Wen Yin
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2008
Source: Tang, W. Y. (2008). Feature selection algorithms for very high dimensional data and mixed data. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Feature selection is an important issue in pattern recognition. The goal of feature selection algorithm is to identify a set of relevant features, based on which to construct a classifier for a pattern recognition problem. This thesis addresses the problem of feature selection for very high dimensional data and mixed data, which exist in many application domains of pattern recognition nowadays. The proposed feature selection algorithms aim to eliminate both irrelevant and redundant features while retaining major discriminating underlying data.
URI: https://hdl.handle.net/10356/41404
DOI: 10.32657/10356/41404
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
TangWenyin08.pdf56.21 MBAdobe PDFThumbnail
View/Open

Page view(s) 5

295
checked on Oct 27, 2020

Download(s) 5

154
checked on Oct 27, 2020

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.