Volume & Issue no: Volume 4, Issue 3, May - June 2015
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Title: |
HYBRID CLASSIFICATION ALGORITHMS FOR TERRORISM PREDICTION in Middle East and North Africa |
Author Name: |
Motaz M. H. Korshid , Tarek H. M. Abou-El-Enien , Ghada M. A. Soliman |
Abstract: |
Abstract
Machine learning methods used for prediction and decision
support are of great concern nowadays. Methods for learning
implicit, non-symbolic knowledge provide better predictive
accuracy. But Methods for learning explicit, symbolic
knowledge produce more comprehensible models. Hybrid
machine learning models combine strengths of both
knowledge representation of model types. In this research we
compare predictive accuracy and comprehensibility of
explicit, implicit, and hybrid machine learning models. This
research based on predicting terrorist groups responsible of
attacks in Middle East and North Africa from year 2009 up to
2013 by comparing various standard, ensemble, hybrid, and
hybrid ensemble machine learning methods namely; Naïve
Bayes, K-nearest neighbours, Decision Tree, Support Vector
Machine; Hybrid Hoeffding Tree, Functional Tree, Hybrid
Naïve Bayes with Decision Table, Classification via
Clustering; Random Forests; and Stacking classifiers.
Afterwards compare the results obtained from conducting the
experiments according to four different performance
measures. Experiments were carried out using real world
data represented by Global terrorism Database (GTD) from
National Consortium for the study of terrorism and
Responses of Terrorism (START).
Keywords: Hybrid Models, Machine Learning, Predictive
Accuracy, Supervised Learning. |
Cite this article: |
Motaz M. H. Korshid , Tarek H. M. Abou-El-Enien , Ghada M. A. Soliman , "
HYBRID CLASSIFICATION ALGORITHMS FOR TERRORISM PREDICTION in Middle East and North Africa" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 4, Issue 3, May - June 2015 , pp.
023-029 , ISSN 2278-6856.
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