MCMI-ANFIS: A robust multi class multiple instance Adaptive Neuro-Fuzzy Inference System
Amine B. Khalifa, Hichem Frigui · 2016
Fuzzy logic is a powerful tool to model knowledge uncertainty, measurements imprecision, and vagueness. However, there is another type of vagueness that arises when data have multiple forms of expression. This is the case for multiple instance learning problems (MIL). In MIL, an object is represented by a collection of instances, called a bag. Labels of bags are known but not those of individual instances. In previous work, we have introduced MI-ANFIS, a Multiple Instance-ANFIS, that extends the standard Adaptive Neuro Fuzzy Inference System (ANFIS) to handle reasoning with bags of instances as input and capable of learning from ambiguously labeled data. In this paper, first, we present a regularization technique, we called Rule Dropout, and show that it could be used to train MI-ANFIS systems with better generalization. Next, we develop a multi class MI-ANFIS (MCMI-ANFIS), that could be used to solve multiple class classification problems effectively. The proposed MCMI-ANFIS is tested and validated using a benchmark data set suitable for multiple class MIL problems.