Ensemble Approach Based on Bagging and Boosting for Identification the Computer System State

S. Gavrylenko, V. Chelak, Oleksii Hornostal
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引用次数: 2

Abstract

The efficiency of using machine learning technology to detect the state of a computer system has been studied. A set of different classifiers and ensembles of classifiers was developed, their training, cross-checking (testing) on real data were carried out. Based on the research results, two methods for identifying the state of a computer system are proposed using an ensemble of decision trees based on boosting and bagging as a classifier. These classifiers were modified due to a special procedure for selecting the optimal parameters for the functioning of the classifiers as well as through the use of the initial data preprocessing procedure. The developed methods are implemented in the software and are investigated in solving the problem of identifying the state of the computer system. The efficiency of the developed classifiers has been evaluated. Prospects for further research may be the development of an ensemble of fuzzy decision trees based on the proposed methods, optimization of their software implementation.
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基于Bagging和Boosting的计算机系统状态识别集成方法
研究了利用机器学习技术检测计算机系统状态的效率。开发了一组不同的分类器和分类器集合,并在实际数据上进行了训练和交叉检验(测试)。在此基础上,提出了两种基于boosting和bagging的决策树集合作为分类器来识别计算机系统状态的方法。由于选择分类器功能的最佳参数的特殊程序以及通过使用初始数据预处理程序,这些分类器被修改。所开发的方法在软件中实现,并在解决计算机系统状态识别问题方面进行了研究。对所开发的分类器的效率进行了评价。进一步研究的前景可能是基于所提出的方法开发模糊决策树集合,优化其软件实现。
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