Dec 14, 2023 · The method is tested on 20 popular datasets and shows superior performance compared to 4 state-of-the-art algorithms. It outperformed all competing methods on 13 datasets with adversarial accuracy metrics, and on all 20 considered datasets with minimax regret. Strong experimental results and flexibility ...

Abstract. In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand ad- versarial attacks, including one of the most widely adopted, efficient, and interpretable ML algorithms—decision trees. (DTs). This paper proposes a novel coevolutionary algorithm.

Dec 14, 2023 · The method is tested on 20 popular datasets and shows superior performance compared to 4 state-of-the-art algorithms. It outperformed all competing methods on 13 datasets with adversarial accuracy metrics, and on all 20 considered datasets with minimax regret.

Objective: Develop robust machine learning models, particularly focusing on decision trees. Algorithm: CoEvoRDT - coevolutionary algorithm designed for creating robust decision trees capable of handling noisy high-dimensional data in adversarial contexts. CoEvoRDT alternately evolves competing popu-.

Coevolutionary Algorithm for Building Robust. Decision Trees under Minimax Regret. Adam Żychowski1, Andrew Perrault2, Jacek Mańdziuk1,3,4. 1Faculty of Mathematics and Information Science, Warsaw University of Technology. 2Department of Computer Science and Engineering, The Ohio State University. 3Faculty of Computer ...

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Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret ... It outperformed all competing methods on 13 datasets with adversarial accuracy metrics, and on all 20 considered datasets with minimax regret.

Apr 25, 2024 · BibSonomy; LinkedIn; Facebook. persistent URL: https://dblp.org/rec/conf/aaai/ZychowskiPM24. Adam Zychowski, Andrew Perrault, Jacek Mandziuk: Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret. AAAI 2024: 21869-21877; 2023. [j7]. view. electronic edition via DOI ...

This paper proposes a novel coevolutionary algorithm (CoEvoRDT) designed to create robust DTs capable of handling noisy high-dimensional data in adversarial contexts. Motivated by the limitations of traditional DT algorithms, we leverage adaptive coevolution to allow DTs to evolve and learn from interactions with ...