Multi-objective optimization of base classifiers in StackingC by NSGA-II for intrusion detection

Multiple Classifier Systems are often found to improve results of intrusion detection by combining a set of classifier decisions where single classifiers may not achieve the same level of detection. However not every set of classifiers is more able, therefore selection of more capable sets is required. A misclassification is a false positive or negative instance; a set of classifiers may produce one more than the other. An optimal set of classifiers is required to reduce both, thus treating them as individual objectives allows a balance to be found. The aim of this work is the selection of optimal sets of base level classifies using an evolutionary computation approach. A comparative analysis is made of the performance of the generated ensembles against the individual base level classifiers, it is shown that optimal ensembles can be found to perform better than a majority of individuals.