Streaming data classification

Abstract

In the evolving technology of big data, high velocity data streams play a vital role since pattern of data is being changed over time. The temporal pattern change in data stream leads to a concept evolution called concept drift where statistical properties of data differs from time to time and the drift is taken into account in order to update old and outdated classifier and make it adaptable to new data arrival and pattern change over. In order to classify the stream data, a scalable efficient classification algorithm is to be designed which perfectly classifies the data with minimizing misclassification rate in presence of concept drift due to high velocity data. Training time of the classifier must be reduced in order to reduce computational complexity. In this work, a novel algorithm has been implemented using Random Forest with stratified random sampling and Bloom filtering in order to reduce the training time and to handle high velocity data. Experimental results are shown by performing classification with sampling, classification with filtering and classification with sampling and filtering. This enhances the performance of the algorithm by decreasing the training time and testing time of the classifier with negligible compromise in accuracy of classification.