Game Theory in Binary Classification: A Nash Equilibrium Decision Tree Model
| dc.contributor.author | ATLAOUI, Amel | |
| dc.date.accessioned | 2026-01-13T08:56:38Z | |
| dc.date.available | 2026-01-13T08:56:38Z | |
| dc.date.issued | 2025 | |
| dc.description | 61f.;ill:30cm | |
| dc.description.abstract | In this thesis, we present the method in [13] which combines game theory and machine learning to develop a new decision tree model for binary classification, called the Nash Equilibrium Decision Tree (NE-DT). Unlike standard decision trees, NE-DT treats the splitting process as a game between two players (left and right branches), where each tries to best separate the data. The Nash equilibrium is a key concept in game theory ,is used to find the optimal split, improving accuracy and robustness. The work explains basic game theory and machine learning concepts, then details the NE-DT algorithm with clear mathematical steps. A simple example using the Iris dataset shows how the model works in practice. The results prove that NE-DT effectively separates classes while being easy to understand. | |
| dc.identifier.uri | https://dspace.ummto.dz/handle/ummto/29522 | |
| dc.language.iso | en | |
| dc.publisher | ummto.faculté des sciences | |
| dc.subject | Game theory | |
| dc.subject | Nash equilibrium | |
| dc.subject | Decision trees | |
| dc.subject | Binary classification | |
| dc.subject | Machine learning | |
| dc.title | Game Theory in Binary Classification: A Nash Equilibrium Decision Tree Model | |
| dc.type | Thesis |