Abstract
This paper is devoted to the new application of the ACDF approach. In this work we propose a new way of an virtual-ant performance evaluation. This approach concentrates on the decision tree construction using ant colony metaphor the goal of experiments is to show that decision trees construction may by oriented not only at accuracy measure. The proposed approach enables (depending on the decision tree quality measure) the decision tree construction with high value of accuracy, recall, precision, F-measure or Matthews correlation coefficient. It is possible due to use of nondeterministic, probabilistic approach - Ant Colony Optimization. The algorithm proposed was examined and the experimental study confirmed that the goal-oriented ACDT can create expected decision trees, accordance to the specified measures.
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Kozak, J., Boryczka, U. (2014). Goal-Oriented Requirements for ACDT Algorithms. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_60
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DOI: https://doi.org/10.1007/978-3-319-11289-3_60
Publisher Name: Springer, Cham
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