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.