Decision trees rank among the most popular classification tools, employed in practical applications due to their known efficiency. A Nash equilibrium-based decision tree splits node data using the Nash equilibrium concept. Boosting is a technique that is used to enhance the performance of a classifier by allowing an in-depth exploration of the data. This…
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 hyperplanes are intuitively appealing and have been widely studied.…
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 they solve or their performance is dependent on the split criterion…
The feature selection problem has become a key undertaking within machine learning. For classification problems, it is known to reduce the computational complexity of parameter estimation, but it also adds an important contribution to the explainability aspects of the results. An evolution strategy for feature selection is proposed in this paper. Feature weights are…
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 label from the list of neighbors of a tested instance. In this…
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 a two-player game to determine the splitting…
Decision trees and random forests are some of the most popular machine learning tools for binary classification, being used in many practical applications. Both methods provide a neighborhood for tested data during the prediction phase, and probabilities are usually computed based on the proportion of classes in those neighborhoods. The approach presented in this…
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 deviations of players that can improve payoffs by unilateral deviations in order to imitate the…
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 Nash equilibrium of this game is approximated by modifying the Covariance Matrix Adaptation Evolution Strategy…
