Two Ways of Extending BRACID Rule-based Classifiers for Multi-class Imbalanced Data

Maria Naklicka, Jerzy Stefanowski
Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 154:90-103, 2021.

Abstract

The number of rule-based classifiers specialized for imbalanced data is quite small so far. In particular, there is no such classifier dedicated for multi-class imbalance data. Thus, in this work we considered two ways of extending BRACID, which is the effective algorithm for binary data. In the first approach, BRACID was used in the OVO ensemble along with modifications of the prediction aggregation strategy. The second approach modifies an induction of rules for multiple classes simultaneously, additionally combined with their post-pruning. Experiments showed that both approaches outperformed the baselines. Moreover, the second approach turned out to be better than OVO with respect to predictive results and producing a smaller number of rules.

Cite this Paper

BibTeX
@InProceedings{pmlr-v154-naklicka21a, title = {Two Ways of Extending BRACID Rule-based Classifiers for Multi-class Imbalanced Data}, author = {Naklicka, Maria and Stefanowski, Jerzy}, booktitle = {Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications}, pages = {90--103}, year = {2021}, editor = {Moniz, Nuno and Branco, Paula and Torgo, Luis and Japkowicz, Nathalie and Woźniak, Michał and Wang, Shuo}, volume = {154}, series = {Proceedings of Machine Learning Research}, month = {17 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v154/naklicka21a/naklicka21a.pdf}, url = {https://proceedings.mlr.press/v154/naklicka21a.html}, abstract = {The number of rule-based classifiers specialized for imbalanced data is quite small so far. In particular, there is no such classifier dedicated for multi-class imbalance data. Thus, in this work we considered two ways of extending BRACID, which is the effective algorithm for binary data. In the first approach, BRACID was used in the OVO ensemble along with modifications of the prediction aggregation strategy. The second approach modifies an induction of rules for multiple classes simultaneously, additionally combined with their post-pruning. Experiments showed that both approaches outperformed the baselines. Moreover, the second approach turned out to be better than OVO with respect to predictive results and producing a smaller number of rules.} }
Endnote
%0 Conference Paper %T Two Ways of Extending BRACID Rule-based Classifiers for Multi-class Imbalanced Data %A Maria Naklicka %A Jerzy Stefanowski %B Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications %C Proceedings of Machine Learning Research %D 2021 %E Nuno Moniz %E Paula Branco %E Luis Torgo %E Nathalie Japkowicz %E Michał Woźniak %E Shuo Wang %F pmlr-v154-naklicka21a %I PMLR %P 90--103 %U https://proceedings.mlr.press/v154/naklicka21a.html %V 154 %X The number of rule-based classifiers specialized for imbalanced data is quite small so far. In particular, there is no such classifier dedicated for multi-class imbalance data. Thus, in this work we considered two ways of extending BRACID, which is the effective algorithm for binary data. In the first approach, BRACID was used in the OVO ensemble along with modifications of the prediction aggregation strategy. The second approach modifies an induction of rules for multiple classes simultaneously, additionally combined with their post-pruning. Experiments showed that both approaches outperformed the baselines. Moreover, the second approach turned out to be better than OVO with respect to predictive results and producing a smaller number of rules.
APA
Naklicka, M. & Stefanowski, J.. (2021). Two Ways of Extending BRACID Rule-based Classifiers for Multi-class Imbalanced Data. Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications, in Proceedings of Machine Learning Research 154:90-103 Available from https://proceedings.mlr.press/v154/naklicka21a.html.

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