Ant colony and particle swarm optimization for financial classification problems

  • Authors:

  • Yannis Marinakis;Magdalene Marinaki;Michael Doumpos;Constantin Zopounidis

  • Affiliations:

  • Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete, University Campus, 73100 Chania, Greece;Industrial Systems Control Laboratory, Department of Production Engineering and Management, Technical University of Crete, University Campus, 73100 Chania, Greece;Financial Engineering Laboratory, Department of Production Engineering and Management, Technical University of Crete, University Campus, 73100 Chania, Greece;Financial Engineering Laboratory, Department of Production Engineering and Management, Technical University of Crete, University Campus, 73100 Chania, Greece

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

Financial 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.