TWO-STAGE BOOSTING OF BINARY CLASSIFICATION BASED ON THE APPLICATION OF BIOINSPIRED ALGORITHMS
Abstract
In the process of solving a wide range of applied problems, it becomes necessary to decompose objects. As a result, the classification problem is an urgent problem in modern data mining systems. Binary classification is one of the most important tasks, and has a number of unsolved problems. One such problem is the effectiveness of automated classification. In the tasks of automated classification, it is relevant to use the algorithmic apparatus of evolutionary computing. Thus, it is advisable to use genetic and bio-inspired algorithms in the task of finding the optimalvalues of the classifier parameters. To solve this problem, it is proposed to apply the particle swarm algorithm (PSO). This algorithm in the context of the task of finding suboptimal values of the parameters of the classifier is able to provide high quality classification. A modification of the algorithm is a dynamic change in the coordinate values that are responsible for the type of kernel function. This revision can significantly reduce the time spent developing the classifier. To increase the classification efficiency, it is advisable to use ensembles of algorithms. The paper presents the structure of a two-level classifier. At the first level of this classifier, an ensemble of simple classifiers is formed that form the training set, which is further used by the particle swarm algorithm in the second stage. This approach can significantly reduce time costs, as well as improve the quality of the resulting solutions. The particle swarm algorithm (PSO), in the context of the task of finding suboptimal values of the parameters of the classifier, is able to provide high quality classification. The proposed two-level algorithm has been experimentally tested. A comparison is made with analogues, comparative charts are given. The described studies show that the work is of high theoretical significance, and the conducted experimental studies prove high practical significance.
