Applied intelligence (Boston) Authors M. Suciu, R. Lung Abstract Decision trees rank among the most popular and efficient classification methods. They are used to represent rules for recursively partitioning the data space into regions from which reliable predictions regarding classes can be made. These regions are usually delimited by axis-parallel or oblique hyperplanes. Axis-parallel…
Logic Journal of the IGPL Authors M. Suciu, R. Lung Abstract Classification and feature selection are two of the most intertwined problems in machine learning. Decision trees (DTs) are straightforward models that address these problems offering also the advantage of explainability. However, solutions that are based on them are either tailored for the problem…
Applied Soft Computing Authors Noémi Gaskó, Tamás Képes, R. Lung, M. Suciu Abstract The Influence Maximization Problem is a challenging computational task with multiple real-world applications. A new approach to this problem based on cooperative game theory and optimization called the Shapley Influence Maximization Extremal Optimization approach is proposed. The influence maximization problem for the independent…
IEEE Symposium Series on Computational Intelligence Authors R. Lung, M. Suciu Abstract K-nearest neighbor is one of the simplest and most intuitive binary classification methods providing robust results on a wide range of data. However, classification results can be improved by using a decision method that is capable of assigning, if necessary, the minority…
Eä Authors R. Lung, M. Suciu Abstract Decision trees are some of the most popular and intuitive classification techniques. Based on the recursive division of the data, the goal is to ultimately identify regions in the space in which most instances belong to the same class. This paper proposes a game-theoretic decision tree using…
International Conference on Machine Learning, Optimization, and Data Science Authors M. Suciu, R. Lung Abstract A game theoretic flavoured decision tree is designed for multi-class classification. Node data is split by using a game between sub-nodes that try to minimize their entropy. The splitting parameter is approximated by a naive approach that explores the…
International Conference on Machine Learning, Optimization, and Data Science Authors M. Suciu, R. Lung Abstract The Nash equilibrium is used to estimate the parameters of a Probit binary classification model transformed into a multiplayer game. Each training data instance is a player of the game aiming to maximize its own log likelihood function. The…