Title :
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
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;
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
DOI :
10.1109/ICMLC.2008.4620442
