@inproceedings{41f5a559880f4391abfe3442f31bf78e,
title = "Rule reduction over numerical attributes in decision trees using multilayer perceptron",
abstract = "Many data sets show significant correlations between input variables, and much useful information is hidden in the data in a non- linear format. It has been shown that a neural network is better than a direct application of induction trees in modeling nonlinear characteristics of sample data. We have extracted a compact set of rules to support data with input variable relations over continuous-valued attributes. Those re- lations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. It is shown in this paper that vari- able thresholds play an important role in constructing linear classifier rules when we use a decision tree over linear classifiers extracted from a multilayer perceptron. We have tested this scheme over several data sets to compare it with the decision tree results.",
author = "Kim, {Dae Eun} and Jaeho Lee",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 ; Conference date: 16-04-2001 Through 18-04-2001",
year = "2001",
doi = "10.1007/3-540-45357-1_57",
language = "English",
isbn = "3540419101",
series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",
publisher = "Springer Verlag",
pages = "538--549",
editor = "David Cheung and Williams, {Graham J.} and Qing Li",
booktitle = "Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings",
address = "Germany",
}