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Optimizing feature selection to improve transport layer statistics quality

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There is significant interest in the network management and industrial security community about the need to improve the quality of transport layer statistics (TLS) and to identify the “best” and most relevant features. The ability to eliminate redundant and irrelevant features is important in order to improve the classification accuracy and to reduce the computational complexity related to the construction of the classifier. In practice, several feature selection (FS) methods can be used as a preprocessing step to eliminate redundant and irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. This chapter investigates the advantages and disadvantages of such FS methods with new proposed metrics, namely goodness, stability, and similarity. The aim here is to come up with an integrated FS method that is built on the key strengths of existing FS methods. A novel way is described to identify efficiently and accurately the “best” features by first combining the results of some well-known FS methods to find consistent features and then use the proposed concept of support to select the smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic datasets demonstrates significant gain in accuracy and improved runtime performance of a classifier compared to individual results of well-known FS methods.

Chapter Contents:

• 6.1 Introduction
• 6.2 FS methods for benchmarking
• 6.3 The new metrics
• 6.4 Experimental methodology
• 6.5 Preliminary experiments
• 6.5.1 Experimental results
• 6.5.2 Discussion
• 6.6 The local optimization approach (LOA)
• 6.6.1 The algorithm
• 6.6.2 An illustrative example
• 6.6.3 Result and analysis
• 6.6.4 Choice of parameters
• 6.6.5 Impact of FS methods on runtime
• 6.6.6 Comparing FS methods computational performance
• 6.6.7 Summary of results with different datasets and limitations of LOA approach
• 6.7 Conclusion

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