@INPROCEEDINGS { Rsds3549,
author = {Gupta, Narendra K. and Ezawa, Kazuo J.}, 
title = {Learning Fraud Detection and Account Treatment Decision Models From Data}, 
booktitle = {Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
conference = {International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
pages = {331--338}, 
publisher = {Japanese Research Group on Rough Sets}, 
address = {Tokyo, Japan}, 
month = {November}, 
year = 1996, 
editor = {Tsumoto, Shusaku and Kobayashi, Satoshi and Yokomori, Takashi and Tanaka, Hiroshi and Nakamura, Akira}, 
isbn = {4-947717-01-7}, 
abstract = {Fraud problem in the telecommunications industry presents two technical challenges: the detection and the treatment of the account given the detection. This paper addresses the discovery of knowledge bearing on the detection and the account treatment decision in the form of Bayesian network models and influence diagrams (normative decision models) using a novel supervised machine learning method that constructs Bayesian network models. Algorithms presented in this paper exploit the goal oriented characteristic of classification tasks and influence diagrams.}, 
keywords = {Bayesian networks, machine learning, influence diagrams, }, 
}