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Evolutionary multiple instance boosting framework for weakly supervised learning
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DOI：10.1007/s40747-021-00469-9.png)
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Abstract
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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.
Keywords:
Differential evolution (DE)
Multiple instance learning (MIL)
Boosting
MILBoost
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