Towards robust SVM training from weakly labeled large data sets
Michał Kawulok, Jakub Nalepa · 2015
Learning from large data sets that contain samples of unknown or incorrect labels becomes increasingly important. Such problems are inherent to many big data scenarios, hence there is a need for developing robust generic approaches to learning from difficult data. In this paper, we propose a new memetic algorithm that evolves samples and labels to select a training set for support vector machines from large, weakly-labeled sets. Our extensive experimental study confirmed that the new method presents high robustness against weakly-labeled data and outperforms other state-of-the-art algorithms.