Share Email Print
cover

Proceedings Paper

Estimating the threshold for maximizing expected gain in supervised discrete Bayesian classification
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

When mining discrete data to train supervised discrete Bayesian classifiers, it is often of interest to determine the best threshold setting for maximizing performance. In this work, we utilize a discrete Bayesian classification model, and a gain function, to determine the best threshold setting for a given number of training data under each class. Results are demonstrated for simulated data by plotting the expected gain versus threshold settings for different numbers of discrete training data. In general, it is shown that the expected gain reaches a maximum at a certain threshold. Further, this maximum point varies with the overall quantization of the data. Additional results are also shown for different gain functions on the decision variable.

Paper Details

Date Published: 13 April 2009
PDF: 7 pages
Proc. SPIE 7344, Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009, 734408 (13 April 2009); doi: 10.1117/12.819067
Show Author Affiliations
Robert S. Lynch, Naval Undersea Warfare Ctr. (United States)
Peter K. Willett, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 7344:
Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009
Belur V. Dasarathy, Editor(s)

© SPIE. Terms of Use
Back to Top