{"id":165113,"date":"2013-06-01T00:00:00","date_gmt":"2013-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/selective-sampling-algorithms-for-cost-sensitive-multiclass-prediction\/"},"modified":"2018-10-16T21:32:20","modified_gmt":"2018-10-17T04:32:20","slug":"selective-sampling-algorithms-for-cost-sensitive-multiclass-prediction","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/selective-sampling-algorithms-for-cost-sensitive-multiclass-prediction\/","title":{"rendered":"Selective sampling algorithms for cost-sensitive multiclass prediction"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, we study the problem of active learning for cost-sensitive multiclass classification. We propose selective sampling algorithms, which process the data in a streaming fashion,querying only a subset of the labels. For these algorithms, we analyze the regret and label complexity when the labels are generated according to a generalized linear model. We establish that the gains of active learning over passive learning can range from none to exponentially large, based on a natural notion of margin. We also present a safety guarantee to guard against model mismatch. Numerical simulations show that our algorithms indeed obtain a low regret with a small number of queries.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we study the problem of active learning for cost-sensitive multiclass classification. We propose selective sampling algorithms, which process the data in a streaming fashion,querying only a subset of the labels. For these algorithms, we analyze the regret and label complexity when the labels are generated according to a generalized linear model. 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