KNN Classifier and Naive Bayse Classifier for Crime Prediction in San Francisco Context
Paper Title:
KNN Classifier and Naive Bayse Classifier for Crime Prediction in San Francisco Context
Authors:
Noora Abdulrahman and Wala Abedalkhader
Department of Engineering Systems and Management, Masdar Institute of Science and Technology, Abu Dhabi, the United Arab Emirates
In this paper we propose an approach for crime prediction and classification using data mining for San Francisco. The approach is comparing two types of classifications: the K-NN classifier and the Naïve Bayes classifier. In the K-NN classifier, two different techniques were performed uniform and inverse. While in the Naïve Bayes, Gaussian, Bernoulli, and Multinomial techniques were tested. Validation and cross validation were used to test the result of each technique. The experimental results show that we can obtain a higher classification accuracy by using multinomial Naïve Bayes using cross validation
Keywords:
Classification, K-NN Classifier, Naïve Bayes, Data Mining, Gaussian, Bernoulli, Multinomial, Uniform, Inverse & Python
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