Combining Chaotic Transformations and Machine Learning Algorithms: Evaluating Explainable Artificial Intelligence Model Performance

Cem Özkurt; Eyüp Altuğ Tunç; Fadime Zeliha Seyhan; Anıl Tunç; Ali Furkan Kamanlı

EN

Combining Chaotic Transformations and Machine Learning Algorithms: Evaluating Explainable Artificial Intelligence Model Performance

Abstract

The research presented covers the synthesis of data analysis, machine learning, and explainable artificial intelligence principles. The study investigates chaotic transformations that affect the performance and interpretability of artificial intelligence models in complex systems. Three different chaotic systems were used to transform features in the dataset, including Lorenz, Chen, and Rossler. These transformed datasets were then analyzed using various machine learning algorithms such as Random Forest, Decision Tree and CatBoost. Performance metrics were calculated to evaluate the effectiveness of each combination. Based on these findings, it was observed that the Rossler chaotic system and CatBoost algorithm gave the best results with %99 accuracy, 0.9997 recall and 0.9997 f1 score. The effects of the transformed data on class labels were elucidated using different explainable artificial intelligence models such as ELI5, DALEX and SHAP. Weighted impact analysis outputs were obtained in the range of 3.5 in the SHAP model, 0.035 in the DALEX model and 0.2796 in the ELI5 model. Among the Explainable Artificial Intelligence models, the ELI5 model, which has a more precise range of values, provided the most consistent explanation in our study. Future studies aim to improve the understanding and prediction capabilities of the model by integrating more chaotic systems and machine learning algorithms. Additionally, investigating the robustness of the proposed approach across various datasets and problem domains is anticipated to provide broader applicability and reliability.

Keywords

Chaotic Systems , Machine Learning , Explainable Artificial Intelligence (XAI) , Chaos Theory , Chaotic transformations.

References

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Details

Primary Language

English

Subjects

Intelligent Robotics

Journal Section

Research Article

Authors

Cem Özkurt ^*
0000-0002-1251-7715
Türkiye

Eyüp Altuğ Tunç This is me
0009-0007-5166-8077
Türkiye

Fadime Zeliha Seyhan This is me
0009-0009-5051-3254
Türkiye

Anıl Tunç
0009-0003-7230-947X
Türkiye

Ali Furkan Kamanlı
0000-0002-4155-5956
Türkiye

Early Pub Date

May 20, 2025

Publication Date

June 22, 2025

Submission Date

October 2, 2024

Acceptance Date

March 21, 2025

Published in Issue

Year 2025 Volume: 8 Number: 1

IZ

https://izlik.org/JA79MS22GZ

IEEE

[1]C. Özkurt, E. A. Tunç, F. Z. Seyhan, A. Tunç, and A. F. Kamanlı, “Combining Chaotic Transformations and Machine Learning Algorithms: Evaluating Explainable Artificial Intelligence Model Performance”, International Journal of Data Science and Applications, vol. 8, no. 1, pp. 45–61, June 2025, [Online]. Available: https://izlik.org/JA79MS22GZ

AI Research and Application Center, Sakarya University of Applied Sciences, Sakarya, Türkiye.

Öz

Combining Chaotic Transformations and Machine Learning Algorithms: Evaluating Explainable Artificial Intelligence Model Performance

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

IZ

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