MI-ANFIS: A multiple instance Adaptive Neuro-Fuzzy Inference System

Abstract

We introduce a novel adaptive neuro-fuzzy architecture based on the framework of Multiple Instance Fuzzy Inference. The new architecture called Multiple Instance-ANFIS (MI-ANFIS), is an extension of the standard Adaptive Neuro Fuzzy Inference System (ANFIS) [1] that is designed to handle reasoning with multiple instances (bags of instances) as input and capable of learning from ambiguously labeled data. In multiple instance problems the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. Multiple Instance Learning (MIL) deals with learning a classifier at the bag level. Over the years many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. In this paper, we develop MI-ANIFS that generalizes ANFIS inference systems to account for ambiguity and reason with multiple instances. We also develop a learning algorithm to learn the parameters of MI-ANFIS. The proposed MI-ANFIS is tested and validated using a synthetic and benchmark data sets suitable for MIL problems.