Stable Classification in Environments with Varying Degrees of Uncertainty

Most practical signal processing problems have to deal with uncertainties, e. g., due to noisy input data. Usual strategies to do this are based on estimating these uncertainties by statistical methods in advance. For some systems with multi-staged signal processing it is possible to identify these estimates at runtime and to relate a degree of certainty to them. If such degrees of certainty are known for input signals, e. g. by earlier stages of processing, this knowledge can be used to get a more robust or accurate result in classification tasks in the later stages, even if they vary at runtime. In this paper we thus introduce an approach to extend support vector machines to incorporate such known uncertainties at runtime, given as certainty degrees. Based on the known certainty of each input, classification depends more on certain inputs and gradually less on uncertain input data. This is done by changing the decision (kernel) function online, i. e., during operation. An artificial two-dimensional dataset is used to visualize the effects of this extension. And the application to three different datasets is a first benchmark showing that the resulting classification quality increases when known uncertainties are considered.