Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5′, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.
%0 Journal Article
%1 noKey
%A Frank, Eibe
%A Wang, Yong
%A Inglis, Stuart
%A Holmes, Geoffrey
%A Witten, IanH.
%D 1998
%I Kluwer Academic Publishers
%J Machine Learning
%K thema:exploiting_place_features_in_link_prediction_on_location-based_social_networks
%N 1
%P 63-76
%R 10.1023/A:1007421302149
%T Using Model Trees for Classification
%U http://dx.doi.org/10.1023/A%3A1007421302149
%V 32
%X Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5′, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.
@article{noKey,
abstract = {Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5′, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.},
added-at = {2014-04-24T15:02:06.000+0200},
author = {Frank, Eibe and Wang, Yong and Inglis, Stuart and Holmes, Geoffrey and Witten, IanH.},
biburl = {https://www.bibsonomy.org/bibtex/23f02890d0e8a2d07b64a4a4305db85f8/b.helmerich},
description = {Using Model Trees for Classification - Springer},
doi = {10.1023/A:1007421302149},
interhash = {32f4c429f136454c7cc615ca7c61d035},
intrahash = {3f02890d0e8a2d07b64a4a4305db85f8},
issn = {0885-6125},
journal = {Machine Learning},
keywords = {thema:exploiting_place_features_in_link_prediction_on_location-based_social_networks},
language = {English},
number = 1,
pages = {63-76},
publisher = {Kluwer Academic Publishers},
timestamp = {2014-04-24T15:02:06.000+0200},
title = {Using Model Trees for Classification},
url = {http://dx.doi.org/10.1023/A%3A1007421302149},
volume = 32,
year = 1998
}