Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies
Abstract
Research in feature selection has paid little attention to unsupervised learning. In this paper we follow the guidelines suggested in previous work by Gennari and present some empirical results in incremental learning of probabilistic concept hierarchies. We identify dierent types of feature selection and justify the use of methods that run in parallel with learning and individually select a dierent set of features for each node in the hierarchy. We use a very simple and inexpensive approach that is exible and powerful enough to explore our proposals. Results indicate that feature selection has a great potential for improving eciency while maintaining or even improving performance. 1.
Cite
Text
Talavera. "Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies." International Conference on Machine Learning, 2000.Markdown
[Talavera. "Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies." International Conference on Machine Learning, 2000.](https://mlanthology.org/icml/2000/talavera2000icml-feature/)BibTeX
@inproceedings{talavera2000icml-feature,
title = {{Feature Selection and Incremental Learning of Probabilistic Concept Hierarchies}},
author = {Talavera, Luis},
booktitle = {International Conference on Machine Learning},
year = {2000},
pages = {951-958},
url = {https://mlanthology.org/icml/2000/talavera2000icml-feature/}
}