Please use this identifier to cite or link to this item: http://monografias.ufrn.br/handle/123456789/8954
Title: A Comparative Analysis of Features Selection Techniques Using Genetic Algorithm in Keystroke Dynamics
Authors: Nascimento, Tuany Mariah Lima do
Keywords: gender recognition;features selection;genetic algorithms
Issue Date: 10-Jun-2019
Publisher: Universidade Federal do Rio Grande do Norte
Citation: NASCIMENTO, Tuany Mariah Lima do. A Comparative Analysis of Features Selection Techniques Using Genetic Algorithm in Keystroke Dynamics. 2019. 36f. Trabalho de Conclusão de Curso (Graduação em Análise e Desenvolvimento de Sistemas) - Unidade Acadêmica Especializada em Ciências Agrárias, Universidade Federal do Rio Grande do Norte, Macaíba, 2019.
Portuguese Abstract: Due to the continuous use of social networks, users can be vulnerable to situations such as paedophilia treats. One of the ways to do the investigation of an alleged paedophile is to verify the legitimacy of the genre that it is said to be. One possible technique to adopt is keystroke dynamics analysis. However, this technique can extract many attributes, causing a negative impact on the accuracy of the classifier due to the presence of redundant and irrelevant attributes. Therefore, the present work presents a comparative analysis between two attribute selection approaches, wrapper and hybrid (wrapper + filter), using the metaheuristic genetic algorithm, as KNN, SVM, and Naive Bayes classifiers and as Correlation and Relief filter. Bringing the best SVM classifier using the wrapper approach, for both databases.
URI: http://monografias.ufrn.br/handle/123456789/8954
Other Identifiers: 20160144419
Appears in Collections:Análise e desenvolvimento de sistemas

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