An Adaptive Classification Framework for Data Streaming Anomaly Detection
Menachem Domb, Guy Leshem · 美中教育评论：B · 2017
Predicting the behavior of a system, we usually analyze its past data to discover common patterns and other classification artifacts.This process consumes considerable computational power and data storage.We propose an approach and a system, which requires much less resources without compromising prediction capabilities and accuracy.It employs three basic methods: common behavior graph, contour surrounding the graph, and entropy calculation methods.When the system is about to be implemented for a specific domain, the optimized combination of these three methods is considered, such that it fits the unique nature of the domain and its corresponding data type.In this work, we propose a framework and a process assisting system designers, finding the optimal methods for the case at hand.We demonstrate our approach with a case study of meteorological data collected over 15 years to classify and detect anomalies in new data.