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http://hdl.handle.net/20.500.12188/30421
Title: | Classification of Crimes Using Machine Learning Techniques for National Crime Data | Authors: | Trpchevska, Marija Dedinec, Aleksandra |
Keywords: | Natural language processing , term frequency-inverse document frequency , TF-IDF , classification , multinomial Naive Bayes , Support Vector Machine | Issue Date: | 22-May-2023 | Publisher: | IEEE | Conference: | 2023 46th MIPRO ICT and Electronics Convention (MIPRO) | Abstract: | The paper aims to assist in a more accurate classification of crimes provided by North Macedonia’s Ministry of Internal Affairs using machine learning techniques. By means of natural language processing, data is transformed into a format suitable for term frequency-inverse document frequency (TF-IDF) vectorization. Bayesian and Support Vector Machine models are utilized and evaluated. The results show non-negligible improvement toward a successful classification of crimes, confirming the beneficial nature of machine learning techniques towards such tasks. | URI: | http://hdl.handle.net/20.500.12188/30421 |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
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