Authors Yu. A. Kravchenko, A. N. Natskevich, I. O. Kursitys
Month, Year 05, 2018 @en
Index UDC 002.53:004.89
Abstract In the article methods of application of boosting models for solving clustering and classification problems are considered, comparative characteristics of these models are described. A boosting model has also been developed to solve the clustering problem. The statement of the problem is given. An analytical review of some promising developments among modern and classical clustering algorithms is presented, their advantages and disadvantages are estimated. A modified boosting algorithm for solving the clustering problem is presented. The approaches of boosting and bagging are compared, the merits and drawbacks of the approaches considered are estimated. The review of algorithms used in the process of boosting is given. As an example of solving the problem of data clustering, a new model for solving optimization problems is presented, based on the use of clustering algorithms weighted set and their boosting based on the ideas of bioinspired algorithms. The heuristic of the proposed boosting algorithm is the use of a probability matrix, which allows a weighted estimation of the learning algorithms quality result to obtain the highest quality of the solution to the clustering problem, and also use weighted data sets containing information on the probability of each individual element occurrence in a particular cluster. The conducted researches showed that the solutions obtained by using the algorithm boosting approach allow to obtain results that are not inferior or superior in quality to the variants obtained by the known algorithms.

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Keywords Boosting; clustering; classification; evolutionary modeling; swarm algorithms; machine learning; bioinspired algorithms.
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