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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.
DOI: 10.32657/10356/41404
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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