NDLI: Feature selection based on scatter degree

Content Provider	IEEE Xplore Digital Library
Author	Jun Ling Xu Bao Wen Xu Cong Wang Zi Feng Cui
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing (Jun Ling Xu; Bao Wen Xu)
Abstract	Feature selection is an important task in machine learning, pattern recognition and data mining. This paper proposed a new feature selection method for classification, named SD, which is based on scatter matrix used in linear discriminant analysis. The main feature of SD is its simplicity and independency of learning algorithms. High-dimensional data samples are first projected into a lower dimensional subspace of the original feature space by means of a linear transformation matrix, which can be attained according to the scatter degree of each feature, and then the scatter degree is used to measure the importance of each feature. A comparison of SD and some popular feature selection methods (information gain and $chi^{2}-test)$ is conducted, and the results of experiment carried out on 19 data sets show the advantages of SD.
Starting Page	417
Ending Page	422
File Size	290600
Page Count	6
File Format	PDF
ISBN	9781424420957
DOI	10.1109/ICMLC.2008.4620442
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Feature extraction Classification algorithms Machine learning Linear discriminant analysis Cybernetics Gain Data mining Feature selection Scatter degree
Content Type	Text
Resource Type	Article

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