MIL-CBR: Case-based Reasoning for Multiple Instance Learning

Abstract

Although ambiguity in label information poses challenges in the multiple-instance learning (MIL) paradigm, it has consistently drawn attention in various fields through the development of machine learning or neural network techniques. These approaches often demonstrate reasonable performance as solutions for MIL problems, but also suffer from a lack of interpretability. Meanwhile, case-based reasoning reinforces interpretation based on inference by identifying key causal factors. Building on this advantage, we propose MIL-CBR with a standard neural network: the neural network directly predicts bag labels by penalizing a positive bag with a lower score compared to a negative bag, where a bag consists of a pair of instances representing heterogeneity measured by Spearman's correlation coefficient. MIL-CBR demonstrates comparable or superior performance against benchmark approaches. While no single approach dominates across all datasets, MIL-CBR showcases the potential of case-based reasoning as an effective solution for MIL problems.