Network traffic classification (NTC) plays an important role in cyber security and network performance, for example intrusion detection facilitating a higher quality of service. However, due to the unbalanced nature datasets, NTC can be extremely challenging poor management degrade performance. While existing methods seek re-balance data distribution through resampling strategies, such approach...

A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitation. These connections are generalized to cost-sensitive classification, in a...

We propose a reinforcement learning based approach to tackle the cost-sensitive learning problem where each input feature has a specific cost. The acquisition process is handled through a stochastic policy which allows features to be acquired in an adaptive way. The general architecture of our approach relies on representation learning to enable performing prediction on any partially observed s...

Cost-sensitive learning is an important issue in both data mining and machine learning, in that it deals with the problem of learning from decision systems relative to a variety of costs. In this paper, we introduce a hierarchy of cost-sensitive decision systems from a test cost perspective. Two major issues are addressed with regard to test cost dependency. The first is concerned with the comm...

This paper is intended to verify that cost-sensitive learning is a competitive approach for learning fuzzy rules in certain imbalanced classification problems. It will be shown that there exist cost matrices whose use in combination with a suitable classifier allows for improving the results of some popular data-level techniques. The well known FURIA algorithm is extended to take advantage of t...