Abstract
This article is devoted to the new application of the ACDT/ACDF algorithms. In this work we distinguish ant colony optimization and join it with decision tree construction algorithms, the proposed approach builds more stable decision forests. Additionally, we would like to mention that it is possible to analyze the overloaded data sets. Several methods are proposed in this study, each considered different pseudo-samples from training data sets. We combine ideas from ACO, Boosting and Random Forests. We show that our algorithms perform comparable to common approaches. Moreover, we demonstrate the suitability of our method to H-bonds detections in protein structures.
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Kozak, J., Boryczka, U. (2013). Dynamic Version of the ACDT/ACDF Algorithm for H-Bond Data Set Analysis. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2013. Lecture Notes in Computer Science(), vol 8083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40495-5_70
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DOI: https://doi.org/10.1007/978-3-642-40495-5_70
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