Evolutionary multiple instance boosting framework for weakly supervised learning

Abstract

Multiple instance boosting (MILBoost) is a framework which uses multiple instance learning (MIL) with boosting technique to solve the problems regarding weakly labeled inexact data. This paper proposes an enhanced multiple boosting framework—evolutionary MILBoost (EMILBoost) which utilizes differential evolution (DE) to optimize the combination of weak classifier or weak estimator weights in the framework. A standard MIL dataset MUSK and a binary classification dataset Hastie_10_2 are used to evaluate the results. Results are presented in terms of bag and instance classification error and also confusion matrix of test data.