1 research outputs found
Semantic concept detection in imbalanced datasets based on different under-sampling strategies
Semantic concept detection is a very useful technique for developing powerful retrieval or filtering systems for multimedia data. To date, the methods for concept detection have been converging on generic classification schemes. However, there is often imbalanced dataset or rare class problems in classification algorithms, which deteriorate the performance of many classifiers. In this paper, we adopt three “under-sampling” strategies to handle this imbalanced dataset issue in a SVM classification framework and evaluate their performances
on the TRECVid 2007 dataset and additional positive
samples from TRECVid 2010 development set. Experimental
results show that our well-designed “under-sampling” methods
(method SAK) increase the performance of concept detection
about 9.6% overall. In cases of extreme imbalance in
the collection the proposed methods worsen the performance
than a baseline sampling method (method SI), however in the
majority of cases, our proposed methods increase the performance of concept detection substantially. We also conclude that method SAK is a promising solution to address the SVM classification with not extremely imbalanced datasets