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Evolving decision-tree induction algorithms with a multi-objective hyper-heuristic using the Pareto dominance approach

Grant number: 15/05218-0
Support type:Research Grants - Visiting Researcher Grant - International
Duration: July 16, 2015 - August 15, 2015
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Márcio Porto Basgalupp
Grantee:Márcio Porto Basgalupp
Visiting researcher: Vili Podgorelec
Visiting researcher institution: University of Maribor (UM), Slovenia
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Associated research grant:10/20255-5 - Genetic programming for evolving decision tree induction algorithms, AP.JP

Abstract

The goal of the proposed research collaboration is to design, develop and evaluate an improved decision-tree induction algorithm with a multi-objective hyper-heuristic using the Pareto dominance approach. The previous results have shown the advantages of evolving a decision-tree induction algorithm with a hyper-heuristic regarding the classification performance. However, the classification accuracy and other classification performance metrics are not the sole indicators of the quality of induced decision trees. One very important advantage of decision trees is their transparent representation of knowledge model, which allows a straightforward and simple interpretation that is close to human thinking. It has been shown in many real-world applications, where the validation of the classification results is as important as the classification itself (like medicine, for example), that the simplicity of a decision tree should not be compromised at the expense of accuracy. The both objectives (accuracy and complexity of a decision tree), however, are generally conflicting. To solve this problem, we propose to use a properly designed and implemented multi-objective optimization approach. (AU)