Evolutionary multiple instance boosting framework for weakly supervised learning
نویسندگان
چکیده
Abstract Multiple instance boosting (MILBoost) is a framework which uses multiple learning (MIL) with technique to solve the problems regarding weakly labeled inexact data. This paper proposes an enhanced framework—evolutionary MILBoost (EMILBoost) utilizes differential evolution (DE) optimize combination of weak classifier or estimator weights in framework. A standard MIL dataset MUSK and binary classification Hastie_10_2 are used evaluate results. Results presented terms bag error also confusion matrix test
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ژورنال
عنوان ژورنال: Complex & Intelligent Systems
سال: 2021
ISSN: ['2198-6053', '2199-4536']
DOI: https://doi.org/10.1007/s40747-021-00469-9