Feature selection based on scatter degree

Author

Xu, Jun-ling ; Xu, Bao-wen ; Wang, Cong ; Cui, Zi-feng

Author_Institution

Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing

Volume

1

fYear

2008

fDate

12-15 July 2008

Firstpage

417

Lastpage

422

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 chi²-test) is conducted, and the results of experiment carried out on 19 data sets show the advantages of SD.

Keywords

data mining; feature extraction; learning (artificial intelligence); matrix algebra; pattern classification; data mining; feature selection; high-dimensional data samples; linear discriminant analysis; linear transformation matrix; lower dimensional subspace; machine learning; pattern recognition; scatter degree; scatter matrix; Cybernetics; Data mining; Feature extraction; Iron; Linear discriminant analysis; Machine learning; Machine learning algorithms; Pattern recognition; Principal component analysis; Scattering; Data mining; Feature selection; Scatter degree;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4620442

Filename

4620442