Fast classification in industrial Big Data environments
Helene Dörksen, Uwe Mönks, Volker Lohweg · 2014
Many modern industrial applications, e.g. those incorporating hundreds or thousands of electrical sensors and actuators, must be categorised into Big Data environments, in which it is essential to design suitable information processing models. Central data processing in such environments is impossible and must be carried out in a distributed way on resource-limited cyber-physical systems. One of the challenging tasks for machine learning is thus the design of a classifier which is simple, accurate and has an acceptable realisation time. We present ComRef-2D-ConvHull method for linear classification optimisation in lower-dimensional feature space, which is based on ComRef from [1]. Compared to original ComRef, we consider only classification optimisation in 2-dimensional feature spaces in ComRef-2D-ConvHull. Due to the decreased time complexity for calculations in 2-dimensional feature space, we expect many industrial Big Data enviroments to profit from our method. Tests regarding the generalisation ability of ComRef-2D-ConvHull on several reference data sets and on a real-world industrial dataset show promising results.