Abstract.
Network data analysis helps in capturing node usage
behavior. Existing algorithms use reduced feature set to manage high runtime
complexity. Ignoring features may increase classification errors. This paper
presents a model, allowing classification of network traffic, while
considering all the relevant features. Learning phase partitions training
sample on values of the respective features. This creates equivalence
classes related to m features. During classification, each feature value
of the test instance results in picking one set from equivalence class
generated during learning. Algorithm captures new behavior in
semi-supervised incremental learning mode. For problems having m features
and n training samples the model has incremental learning complexity of
and average classification complexity is of the
order 
.
© 2012 by Walter de Gruyter Berlin Boston
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