@inproceedings{bc13650d4da04bef9f2a69461fdd799f,
title = "Improving supervised learning by feature decomposition",
abstract = "This paper presents the Feature Decomposition Approach for improving supervised learning tasks. While in Feature Selection the aim is to identify a representative set of features from which to construct a classification model, in Feature Decomposition, the goal is to decompose the original set of features into several subsets. A classification model is built for each subset, and then all generated models are combined. This paper presents theoretical and practical aspects of the Feature Decomposition Approach. A greedy procedure, called DOT (Decomposed Oblivious Trees), is developed to decompose the input features set into subsets and to build a classification model for each subset separately. The results achieved in the empirical comparison testing with well-known learning algorithms (like C4.5) indicate the superiority of the feature decomposition approach in learning tasks that contains high number of features and moderate numbers of tuples.",
keywords = "Feature Selection, Terminal Node, Unlabeled Data, Target Attribute, Generalization Error",
author = "Oded Maimon and Lior Rokach",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 2nd International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2002 ; Conference date: 20-02-2002 Through 23-02-2002",
year = "2002",
month = jan,
day = "1",
doi = "10.1007/3-540-45758-5_12",
language = "English",
isbn = "3540432205",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "178--196",
editor = "Thomas Eiter and Klaus-Dieter Schewe",
booktitle = "Foundations of Information and Knowledge Systems - 2nd International Symposium, FoIKS 2002, Proceedings",
address = "Germany",
}