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• W2312260026 abstract "In many real-world applications, different types of misclassification usually suffer from different costs, but the accurate cost is often hard to be determined and usually one can only get an interval-estimation like that one type of mistake is about 5 to 10 times more serious than the other type. On the other hand, there are usually abundant unlabeled data available, leading to great research effort about semi-supervised learning. It is noticeable that cost interval and unlabeled data usually appear simultaneously in practice tasks; however, there is rare study tackling them together. In this paper, we propose the cisLDM approach which is able to handle cost interval and exploit unlabeled data in a principled way. Rather than maximizing the minimum margin like traditional large margin classifiers, cisLDM tries to optimize the margin distribution on both labeled and unlabeled data when minimizing the worst-case totalcost and the mean total-cost simultaneously according to the cost interval. Experiments on a broad range of datasets and cost settings exhibit the impressive performance of cisLDM. In particular, cisLDM is able to reduce 47 percent more total-cost than standard SVM and 27 percent more total-cost than cost-sensitive semi-supervised SVM which assumes the true cost value is known in advance." @default.
• W2312260026 created "2016-06-24" @default.
• W2312260026 creator A5010751143 @default.
• W2312260026 creator A5076830444 @default.
• W2312260026 date "2016-07-01" @default.
• W2312260026 modified "2023-10-13" @default.
• W2312260026 title "Large Margin Distribution Learning with Cost Interval and Unlabeled Data" @default.
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• W2312260026 doi "https://doi.org/10.1109/tkde.2016.2535283" @default.
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