Ant colony and particle swarm optimization for financial classification problemsAnt colony and particle swarm optimization for financial classification problems Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-092009enFinancial decisions are often based on classification models which are used to assign a set of observations into predefined groups. Such models ought to be as accurate as possible. One important step towards the development of accurate financial classification models involves the selection of the appropriate independent variables (features) which are relevant for the problem at hand. This is known as the feature selection problem in the machine learning/data mining field. In financial decisions, feature selection is often based on the subjective judgment of the experts. Nevertheless, automated feature selection algorithms could be of great help to the decision-makers providing the means to explore efficiently the solution space. This study uses two nature-inspired methods, namely ant colony optimization and particle swarm optimization, for this problem. The modelling context is developed and the performance of the methods is tested in two financial classification tasks, involving credit risk assessment and audit qualifications.http://creativecommons.org/licenses/by/4.0/Expert Systems with Applications36710604-10611 Marinakis Ioannis Μαρινακης Ιωαννης Marinaki Magdalini Μαρινακη Μαγδαληνη Michael Doumpos Δουμπος Μιχαλης Zopounidis Konstantinos Ζοπουνιδης Κωνσταντινος Elsevier Ant colony optimization Particle swarm optimization Feature selection Nearest neighbour classifiers Credit risk assessment Auditing