Positive and unlabeled learning in categorical data
نویسندگان
چکیده
منابع مشابه
Positive and unlabeled learning in categorical data
In common binary classification scenarios, the presence of both positive and negative examples in training data is needed to build an efficient classifier. Unfortunately, in many domains, this requirement is not satisfied and only one class of examples is available. To cope with this setting, classification algorithms have been introduced that learn from Positive and Unlabeled (PU) data. Origin...
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Learning from positive and unlabeled examples (PU learning) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set of positive data and no negative data i...
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In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e., ordinary supervised learning). Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. In this paper, we theoretically compare PU (and NU) learning against PN learning...
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Yixing Xu†, Chang Xu‡, Chao Xu†, Dacheng Tao‡ †Key Laboratory of Machine Perception (MOE), Cooperative Medianet Innovation Center, School of Electronics Engineering and Computer Science, PKU, Beijing 100871, China ‡UBTech Sydney AI Institute, The School of Information Technologies, The University of Sydney, J12, 1 Cleveland St, Darlington, NSW 2008, Australia [email protected], [email protected]...
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In this report, I presented my results to the tasks of 2008 UC San Diego Data Mining Contest. This contest consists of two classification tasks based on data from scientific experiment. The first task is a binary classification task which is to maximize accuracy of classification on an evenly-distributed test data set, given a fully labeled imbalanced training data set. The second task is also ...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2016
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2016.01.089