Using upper bounds on attainable discrimination to select discrete valued features

Lovell, D. R., Dance, C. R., Niranjan, M., Prager, R. W., & Dalton, K. J. (1996) Using upper bounds on attainable discrimination to select discrete valued features. In Neural Networks for Signal Processing [1996] VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop, IEEE, Kyoto, pp. 233-242.

View at publisher


Selection of features that will permit accurate pattern classification is a difficult task. However, if a particular data set is represented by discrete valued features, it becomes possible to determine empirically the contribution that each feature makes to the discrimination between classes. This paper extends the discrimination bound method so that both the maximum and average discrimination expected on unseen test data can be estimated. These estimation techniques are the basis of a backwards elimination algorithm that can be use to rank features in order of their discriminative power. Two problems are used to demonstrate this feature selection process: classification of the Mushroom Database, and a real-world, pregnancy related medical risk prediction task - assessment of risk of perinatal death.

Impact and interest:

6 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 79895
Item Type: Conference Paper
Refereed: Yes
Keywords: Algorithms, Calculations, Data reduction, Errors, Estimation, Pattern recognition, Testing, Discrete valued features, Discrimination bound method, Feature selection process, Neural networks
DOI: 10.1109/NNSP.1996.548353
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: IEEE
Deposited On: 07 Jan 2015 05:31
Last Modified: 07 Jan 2015 05:31

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page