Abstract
Artificial neural network (ANN) and support vector machine (SVM) based classifier design by a meta-heuristic called Co-Operation of Biology Related Algorithms (COBRA) is presented. For the ANN’s structure selection the modification of COBRA that solves unconstrained optimization problems with binary variables is used. The ANN’s weight coefficients are adjusted with the original version of COBRA. For the SVM-based classifier design the original version of COBRA and its modification for solving constrained optimization problems are used. Three text categorization problems from the DEFT’07 competition were solved with these techniques. Experiments showed that all variants of COBRA demonstrate high performance and reliability in spite of the complexity of the solved optimization problems. ANN-based and SVM-based classifiers developed in this way outperform many alternative methods on the mentioned benchmark classification problems. The workability of the proposed meta-heuristic optimization algorithms was confirmed.
Research is fulfilled with the support of the Ministry of Education and Science of Russian Federation within State assignment project 140/14.
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Akhmedova, S., Semenkin, E. (2014). Data Mining Tools Design with Co-operation of Biology Related Algorithms. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_56
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DOI: https://doi.org/10.1007/978-3-319-11857-4_56
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