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## Optimality and stability of feature set for traffic classification

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Feature selection (FS) methods can be used as a preprocessing step to eliminate meaningless features, and also as a tool to reveal the set of optimal features. Unfortunately, as detailed in Chapter 6, such methods are often sensitive to a small variation in the traffic data collected over different periods of time. Thus, obtaining a stable feature set is crucial in enhancing the confidence of network operators. This chapter describes a robust approach, called global optimization approach (GOA), to identify both optimal and stable features, relying on a multi-criterion fusion-based FS method and an information-theoretic method. GOA first combines multiple well-known FS methods to yield possible optimal feature subsets across different traffic datasets and then uses the proposed adaptive threshold, which is based on entropy to extract the stable features. A new goodness measure is proposed within a random forest framework to estimate the final optimum feature subset. The effectiveness of GOA is demonstrated through several experiments on network traffic data in spatial and temporal domains. Experimental results show that GOA provides up to 98.5% accuracy, exhibits up to 50% reduction in the feature set size, and finally speeds up the runtime of a classifier by 50% compared with individual results produced by other well-known FS methods.

Chapter Contents:

• 7.1 Introduction
• 7.2 Optimality versus stability
• 7.2.1 Selecting feature set from global perspective
• 7.2.2 An initial investigation
• 7.3 GOA—the global optimization approach
• 7.3.1 Integration of feature selection
• 7.3.3 Intensive search approach
• 7.4 Evaluation
• 7.4.1 GOA evaluation based on the proposed metrics
• 7.4.2 Comparison between GOA, FCBF-NB, and BNN
• 7.4.3 Relevance of selected features
• 7.4.4 Temporal decay and spatial robustness
• 7.5 Impact of the candidate features on different ML algorithms
• 7.5.1 The sensitivity of the candidate features on different ML algorithms
• 7.5.2 Discretization to improve classification accuracy
• 7.5.3 Impact of discretizing the candidate features
• 7.6 Conclusion

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