Reduction from Cost-Sensitive Ordinal Ranking to Weighted Binary Classification
نویسندگان
چکیده
منابع مشابه
Reduction from Cost-Sensitive Ordinal Ranking to Weighted Binary Classification
We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary ...
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We study the ordinal ranking problem in machine learning. The problem can be viewed as a classification problem with additional ordinal information or as a regression problem without actual numerical information. From the classification perspective, we formalize the concept of ordinal information by a cost-sensitive setup, and propose some novel cost-sensitive classification algorithms. The alg...
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Many real-world applications require varying costs for different types of mis-classification errors. Such a cost-sensitive classification setup can be very different from the regular classification one, especially in the multiclass case. Thus, traditional meta-algorithms for regular multiclass classification, such as the popular one-versus-one approach, may not always work well under the cost-s...
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A class of problems between classification and regression, learning to predict ordinal classes, has not received much attention so far, even though there are many problems in the real world that fall into that category. Given ordered classes, one is not only interested in maximizing the classification accuracy, but also in minimizing the distances between the actual and the predicted classes. T...
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A widespread idea to attack ranking works by reducing it into a set of binary preferences and applying well studied classification techniques. The basic question addressed in this paper relates to whether an accurate classifier would transfer directly into a good ranker. In particular, we explore this reduction for subset ranking, which is based on optimization of DCG metric (Discounted Cumulat...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 2012
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco_a_00265