Smooth Quadrature-Inspired Generalized Choquet Integral in an Application to Anomaly Detection

In this study, we consider a new approach to the enhancement of classic Choquet integral as a vehicle in the processes of aggregation of classifiers or information fusion. The improvement of classification result on a basis of classifier ensambles is one of the most important tasks of machine learning research community. In the previous series of works, we have introduced a conception of building Choquet-like aggregation operator using the idea inspired by one of the most common numerical methods, namely quadratures. Here, we extend this technique by using the concept which we call smoothing. We use this term to express the idea of smoothing the function under the integral symbol, and thus triggering processes that increase the elasticity of the Choquet integral. In a series of numerical experiments with anomaly detection problem, we show that the new approach is better than the existing ones in terms of accuracy and f1 score.