Classification model based on mRMR and factorization machines algorithm

Abstract:

Many scholars have made some achievements in aggregation analysis of terrorist events by using the data set of "Global Terrorism Research Database"(GTD) with game theory, knearestneighbor method and support vector machine. However, data sparsity and highdimensional multiredundancy are not well considered in the previous research, which may lead to low accuracy of clustering classification. This paper proposes a TFM classification model based on "Minimalredundancy maximalrelevancy" (mRMR) combined with " Factorization Machines " (FM), in which the incremental search method is used to find approximately optimal features to address the highdimensional multiredundancy and the data sparsity is tackled with FM method. TFM model is then used to make quantitative classification on the preprocessed terrorist attack data. The experimental results show the proposed TFM model, in terms of Matthews correlation coefficient (MCC), is increased by 49.9%, 2.5% and 2.3% respectively compared with naive Bayes (NB), support vector machine (SVM) and logistic regression (LR). The comparative result demonstrates that TFM model is feasible to some extent.