Success based locally weighted Multiple Kernel combination

Abstract

Multiple Kernel Learning (MKL) literature has mostly focused on learning weights for base kernel combiners. Recent works using instance dependent weights have resulted in better performance compared to fixed weight MKL approaches. This may be attributed to the fact that, different base kernels have varying discriminative capabilities in distinct local regions of input space. We refer to the zones of classification expertize of base kernels as their “Regions of Success” (RoS). We propose to identify and model them (during training) through a set of instance dependent success prediction functions (SPF) having high values in RoS (and low, otherwise). During operation, the use of these SPFs as instance dependent weighing functions promotes locally discriminative base kernels while suppressing others. We have experimented with 21 benchmark datasets from various domains having large variations in terms of dataset size, interclass imbalances and number of features. Our proposal has achieved higher classification rates and balanced performance (for both positive and negative classes) compared to other instance dependent and fixed weight approaches.