DocumentCode :
2345190
Title :
Non-parametric discriminatory power
Author :
Holz, Hilary J. ; Loew, Murray H.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., George Washington Univ., Washington, DC, USA
fYear :
1994
fDate :
27-29 Oct 1994
Firstpage :
65
Abstract :
Discriminatory power is the relative usefulness of a feature for classification. Traditionally feature-selection techniques have defined discriminatory power in terms of a particular classifier. Non-parametric discriminately power allows feature selection to be based on the structure of the data rather than on the requirements of any one classifier. In previous research, we have defined a metric for non-parametric discriminatory power called relative feature importance (RFI). In this work, we explore the construction of RFI through closed-form analysis and experimentation. The behavior of RFI is also compared to traditional techniques
Keywords :
data structures; feature extraction; pattern classification; closed-form analysis; data structure; experimentation; feature classification; feature selection techniques; nonparametric discriminatory power; relative feature importance; Algorithm design and analysis; Computer science; Data mining; Diseases; Eigenvalues and eigenfunctions; Feature extraction; Radiofrequency interference; Scattering; System testing; Zinc;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location :
Alexandria, VA
Print_ISBN :
0-7803-2761-6
Type :
conf
DOI :
10.1109/WITS.1994.513894
Filename :
513894
Link To Document :
بازگشت