Кафедри
Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/35393
Переглянути
2 результатів
Результати пошуку
Документ Method of Identifying the State of Computer System under the Condition of Fuzzy Source Data(2020) Gavrylenko, Svitlana; Chelak, Viktor; Kazarinov, MichaelThe purpose of this work is developing a method for identifying the abnormal state of a computer system based on the Bayes' Fuzzy classifier. It allowed us to create a Fuzzy expert identification system with an unlimited number of controlled indicators that belong to a finite interval. Estimation of informativeness of such indicators does not depend on the type of indicator’s functions and on the rule of their usage in the calculated formula. Introduced criterion allowed to estimate indices of the functioning of computer systems presented indistinctly. The quality of classification was evaluated based on ROC analysis. It was found that the method based on Bayes' Fuzzy expert system is qualitative, and its classification speed is almost independent of quantity indicators. Comparative evaluation of Bayes' Fuzzy classifier with Fuzzy clustering classifier and Fuzzy discriminant classifier are performed. In order to regulate the level of false-positive and false-negative classification, recommendations have been developed to manage the level of sensitivity and specificity of a Fuzzy expert system based on the Bayes classifier.Документ The ensemble method development of classification of the computer system state based on decisions trees(Національний технічний університет "Харківський політехнічний інститут", 2020) Gavrylenko, Svitlana; Sheverdin, Illia; Kazarinov, MichaelThe subject of this article is exploration of methods for identifying the status of a computer system.The purpose of the article is development of a method for classifying a computer system anomalous state based on ensemble methods. Task: To investigate the usage of algorithms for building decision trees: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump and bagging and boosting decision tree ensembles to identify a computer system anomalous state by analyzing operating system events. The methods used are artificial intelligence, machine learning and ensemble classification methods. The following results were obtained: the methods of identifying the computer systems anomalous state based on ensemble methods were investigated, namely, bagging, boosting, and classifiers: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump to identify a computer system anomalous state. The different classifiers set and classifiers ensembles were developed. Training and cross-validation on each algorithm was performed. The developed classifiers performance has been evaluated. The research suggests an ensemble method ofa computer system state classifying based on the J48 decision tree algorithm. Conclusions.The scientific novelty of the obtained results consists in creating an ensemble method for classifying the state of a computer system based on a decision tree, which makes it possible to increase the reliability and speed of classification.