@inproceedings{cac1b496ffe347f2a4d6b9203fd827e7,
title = "LEFRA: Learning from Associations",
abstract = "In data mining, a Multi-level Association Analysis (MAA) produces a set of association rules. These rules mainly identify those values of multiple attributes that arc associated to each other. In this paper, we introduce a new learning paradigm based on association rules called {"}Learning from Association (LEFRA){"} which is used as a part of a predictive system to predict the effect of a number of carcinogens on liver. The validity of the proposed learning paradigm is established by comparing its performance with the performance of logistic regression which has been applied on the same datasct.",
keywords = "Association Analysis, Data Mining, Learning from Association, Predictive Systems",
author = "Hashemi, {Ray R.} and Louis LeBlanc and Westgeest, {Bart J.} and Tyler, {Alexander A.}",
year = "2004",
language = "English",
isbn = "1889335231",
series = "Soft Computing with Industrial Applications - Proceedings of the Sixth Biannual World Automation Congress",
pages = "549--554",
editor = "M. Jamshidi and M. Reuter and D. Andina and J.S. Jamshidi",
booktitle = "Soft Computing with Industrial Applications - International Symposium on Soft Computing for Industry, ISSCI - Proceedings of the Sixth Biannual World Automation Congress, WAC 2004",
note = "Soft Computing with Industrial Applications - International Symposium on Soft Computing for Industry, ISSCI - Sixth Biannual World Automation Congress, WAC 2004 ; Conference date: 28-06-2004 Through 01-07-2004",
}