FEATURE SELECTION BASED ON SCATTER DEGREE

摘要：

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 SI), 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 χ2-test) is conducted, and the results of experiment carried out on 19 data sets show the advantages of SD.

关键词： Feature selection Scatter degree Data mining

作者: JUN-LING XU BAO-WEN XU CONG WANG ZI-FENG CUI

作者单位: School of Computer Science and Engineering, Southeast University, Nanjing 211189, China School of Computer Science and Engineering, Southeast University, Nanjing 211189, China State Key La Ricoh Imaging Technology (Shanghai) Co., Ltd, Shanghai 200233, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 417-422

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）