Abstract
We develop a filter based feature selection approach in Multi-classification by optimizing the so called Generic Feature Selection (GeFS) measure and then using Multi Support Vector Machine (MSVM) classifiers. The problem is first formulated as a polynomial mixed 0-1 fractional programming and then equivalently transformed into a mixed 0-1 linear programming (M01LP) problem. DCA (Difference of Convex functions Algorithm), an innovative approach in nonconvex programming framework, is investigated to solve the M01LP problem. The proposed algorithm is applied on Intrusion Detection Systems (IDSs) and experiments are conducted through the benchmark KDD Cup 1999 dataset which contains millions of connection records audited and includes a wide variety of intrusions simulated in a military network environment. We compare our method with an embedded based method for MSVM using l 2 − l 0 regularizer. Preliminary numerical results show that the proposed algorithm is comparable with l 2 − l 0 regularizer MSVM on the ability of classification but requires less computation.
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References
KDD Cup 1999 data set (1999), http://www.sigkdd.org/kddcup/index.php?section=1999&method=data
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Le Thi, H.A., Le, A.V., Vo, X.T., Zidna, A. (2014). A Filter Based Feature Selection Approach in MSVM Using DCA and Its Application in Network Intrusion Detection. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_42
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DOI: https://doi.org/10.1007/978-3-319-05458-2_42
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