Indução de Árvore de Decisão utilizando Meta-Aprendizado
Symbolic learning models stand out within the Machine Learning area because their representations are interpretable by humans. A feature of this model is very responsive to the set of examples used, which can result in a significant change in the model if there are small variations in the training set. The model combination strategy (ensembles) is presented as an alternative to improve the accuracy and stability of the models. The strategy consists of generating different models from the same training set and combining them into a single final model, usually through a voting process. An undesirable characteristic of the set strategy is the complexity of the final model, since it is formed by a set of models. In this research, an approach is proposed to induce a decision meta-tree based on the combination of decision trees from one (Random Forest). The experiments were performed on 150 datasets from different domains. The proposed approach applied to 43 sets of categorical data out of 150 analyzed, obtained a performance as good as a forest with 128 trees without statistically significant differences. This is an important result, considering the interpretability provided by a single decision tree as the resulting model.
T. G. Dietterich. 1997. Machine Learning Research: Four Current Directions. [link].
Pedro Domingos. 1997. Knowledge acquisition from examples via multiple models. In MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-. MORGAN KAUFMANN PUBLISHERS, INC., 98–106.
Caique Augusto Ferreira, Adriano Henrique Cantão, and José Augusto Baranauskas. 2022. Decision Tree Induction Through Meta-learning. In Artificial Intelligence Applications and Innovations, Ilias Maglogiannis, Lazaros Iliadis, John Macintyre, and Paulo Cortez (Eds.). Springer International Publishing, Cham, 101–111. https://doi.org/10.1007/978-3-031-08337-2_9.
Riccardo Guidotti, Anna Monreale, Fosca Giannotti, Dino Pedreschi, Salvatore Ruggieri, and Franco Turini. 2019. Factual and counterfactual explanations for black box decision making. IEEE Intelligent Systems 34, 6 (2019), 14–23.
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018. A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51, 5 (2018), 1–42.
David Gunning. 2017. Explainable artificial intelligence (xai). Defense advanced research projects agency (DARPA), nd Web 2, 2 (2017), 1.
Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Witten. 2009. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter 11, 1 (2009), 10–18.
Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. 2021. Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy 23, 1 (2021).
T. M. Oshiro, P. S. Perez, and José Augusto Baranauskas. 2012. How Many Trees in a Random Forest?. In Proceedings of the 8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012, Lecture Notes in Computer Science, ISBN 978-3-642-31536-7, Vol. 7376. Berlin, Germany, 154–168. http://dx.doi.org/10.1007/978-3-642-31537-4_13.
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. " Why should i trust you?" Explaining the predictions of any classifier. In Proc. of the 22nd ACM SIGKDD Int. Conf. on knowledge discovery and data mining. 1135–1144.
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135–1144.
Naphaporn Sirikulviriya and Sukree Sinthupinyo. 2011. Integration of rules from a random forest. In International Conference on Information and Electronics Engineering, Vol. 6. 194–198.
Pedro Strecht, Joao Mendes-Moreira, and Carlos Soares. 2014. Merging Decision Trees: a case study in predicting student performance. In International Conference on Advanced Data Mining and Applications. Springer, 535–548.
COUNCIL OF EUROPEAN UNION. 2016. Council regulation (EU) no 279/2016 - Official website of the European Union. [link]
Joaquin Vanschoren. 2018. Meta-learning: A survey. arXiv preprint arXiv:1810.03548 (2018).