Scope Classification: An Instance-Based Learning Algorithm with a Rule-Based Characterisation
Abstract
Scope classification is a new instance-based learning (IBL) technique with a rule-based characterisation. Within the scope approach, the classification of an object o is based on the examples that are closer to o than every example labelled with another class. In contrast to standard distance-based IBL classifiers, scope classification relies on partial preorderings ≤_o between examples, indexed by objects. Interestingly, the notion of closeness to o that is used characterises the classes predicted by all the rules that cover o and are relevant and consistent for the training set. Accordingly, scope classification is an IBL technique with a rule-based characterisation. Since rules do not have to be explicitly generated, the scope approach applies to classification problems where the number of rules prevents them from being exhaustively computed.
Cite
Text
Lachiche and Marquis. "Scope Classification: An Instance-Based Learning Algorithm with a Rule-Based Characterisation." European Conference on Machine Learning, 1998. doi:10.1007/BFB0026697Markdown
[Lachiche and Marquis. "Scope Classification: An Instance-Based Learning Algorithm with a Rule-Based Characterisation." European Conference on Machine Learning, 1998.](https://mlanthology.org/ecmlpkdd/1998/lachiche1998ecml-scope/) doi:10.1007/BFB0026697BibTeX
@inproceedings{lachiche1998ecml-scope,
title = {{Scope Classification: An Instance-Based Learning Algorithm with a Rule-Based Characterisation}},
author = {Lachiche, Nicolas and Marquis, Pierre},
booktitle = {European Conference on Machine Learning},
year = {1998},
pages = {268-279},
doi = {10.1007/BFB0026697},
url = {https://mlanthology.org/ecmlpkdd/1998/lachiche1998ecml-scope/}
}