@inproceedings{0784a654f8fe4d079a48bb1dac958056,
title = "Handling continuous-valued attributes in decision tree with neural network modeling",
abstract = "Induction tree is useful to obtain a proper set of rules for a large amount of examples. However, it has difficulty in obtaining the relation between continuous-valued data points. Many data sets show significant correlations between input variables, and a large amount of useful information is hidden in the data as nonlinearities. It has been shown that neural network is better than direct application of induction tree in modeling nonlinear characteristics of sample data. It is proposed in this paper that we derive a compact set of rules to support data with input variable relations. Those relations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. This will also solve overgeneralization amd overspecialization problems often seen in induction tree. We have tested this scheme over several data sets to compare with decision tree results.",
author = "Kim, {Dae Eun} and Jaeho Lee",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2000.; 11th European Conference on Machine Learning, ECML 2000 ; Conference date: 31-05-2000 Through 02-06-2000",
year = "2000",
doi = "10.1007/3-540-45164-1_22",
language = "English",
isbn = "9783540451648",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "211--219",
editor = "{de Mantaras}, {Ramon Lopez} and Enric Plaza",
booktitle = "Machine Learning",
address = "Germany",
}