Estimating the threshold for maximizing expected gain in supervised discrete Bayesian classification

Robert S. Lynch Jr.; Peter K. Willett

doi:10.1117/12.819067

13 April 2009 Estimating the threshold for maximizing expected gain in supervised discrete Bayesian classification

Proceedings Volume 7344, Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009; 734408 (2009) https://doi.org/10.1117/12.819067
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Robert S. Lynch Jr. and Peter K. Willett "Estimating the threshold for maximizing expected gain in supervised discrete Bayesian classification", Proc. SPIE 7344, Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009, 734408 (13 April 2009); https://doi.org/10.1117/12.819067

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