Classification, game theory and feature engineering: new models and applications


Grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-III-P1-1.1-TE-2021-1374

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The classification problem is a central one in machine learning with multiple applications in economics, finance, politics, medicine, engineering, biology, etc. During the last years more emphasis has been placed on large data rather than focusing on details, leading to possible loss of important information. Recent increase in computational power allows the exploration of new solution concepts, providing a more nuanced insight into the classification. Game theory offers a plethora of equilibria concepts designed for various strategic situations for which the concept of optimality does not work. The main goal of this project is to explore and improve the use of game theoretic concepts in solving supervised and unsupervised classification problems with large number of features. The supervised classification problem is converted into a game, in which data instances are players that choose their class such that the Nash equilibrium of the game represents the correct classification. Parameters of probabilistic models can be estimated to approach the equilibrium of the game. The model can be evolved by using from mechanism design and genetic programming. Genetic programming tools will also be used for automated feature engineering. The unsupervised classification problem will be approached by converting the data into networks and identify clusters/communities and important features/critical nodes by combining computational intelligence tools with suitable equilibria concepts.

Meet us!

The Researchers

Dr. Mihai Suciu


Main interests: evolutionary optimization, game theory, web services composition

Dr. Gaskó Noémi


Main interests: computational game theory, evolutionary algorithms

Dr. Rodica Ioana Lung


Main interests: game theory, evolutionary algorithms

phd. student Bándi Nándor


Main interests: continuous nonlinear optimization, hyper-heuristics, parallel algorithms



Suciu, M., Lung, R.I. (2023). A New Filter Feature Selection Method Based on a Game Theoretic Decision Tree. In: Abraham, A., Hong, TP., Kotecha, K., Ma, K., Manghirmalani Mishra, P., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2022. Lecture Notes in Networks and Systems, vol 647. Springer, Cham.

Suciu, MA., Lung, R.I. (2023). Feature Selection Based on a Decision Tree Genetic Algorithm. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham.

Rodica Ioana Lung, Mihai Suciu. An Evolutionary Approach to Feature Selection and Classification. LOD 2023. (articol acceptat)

Mihai Suciu, Rodica Ioana Lung. A game theoretic decision forest for feature selection and classificatio. Logic Journal of the IGPL. (articol acceptat)

Let's Get In Touch!

Mihai Suciu

Centre for the Study of Complexity, A14

Str. Fantanele, Nr. 30, RO - 400294, Cluj

+40 264 405300