%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 }