Critical parameter analysis of Vertical Hoeffding Tree for optimized performance using SAMOA

Streaming classification of big data is a method under stream data mining that learns from continuous, ordered sequences of data streams coming from diversified sources using limited computing and storage capabilities. SAMOA stands for scalable advanced massive online analysis, is a machine learning framework used to perform distributed data mining over streaming data. Vertical Hoeffding Tree (VHT) under SAMOA is a variant of very fast decision tree used for distributed classification of data streams. The performance of VHT depends on various critical parameters such as tie-threshold, grace value, confidence, split criterion, etc. Although, VHT is widely accepted as an efficient streaming classifier but one of the challenges in streaming classification is varying distribution of incoming data instances with respect to underlying classes in different datasets; therefore performance of VHT varies in different datasets. Therefore, achieving optimal performance from the stream classifier like VHT on different datasets is a challenging task and fixed set of values of critical parameters cannot be preconfigured for various types of datasets. This research work explores the capabilities of VHT streaming classifier of SAMOA in the light of various benchmarking performance statistics such as classification accuracy, kappa and kappa temporal. The work presented here, experimentally identifies suitable values of critical parameters of VHT that yield optimized performance on different datasets. Thus, this analytical study is extremely significant in developing streaming classifiers which achieve optimum performance via parameter tuning at run time.