Minimizing data consumption with sequential online feature selection
نویسندگان
چکیده
منابع مشابه
Minimizing data consumption with sequential online feature selection
In most real-world information processing problems, data is not a free resource. Its acquisition is often expensive and time-consuming. We investigate how such cost factors can be included in supervised classification tasks by deriving classification as a sequential decision process and making it accessible to Reinforcement Learning. Depending on previously selected features and the internal be...
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Online feature selection with dynamic features has become an active research area in recent years. However, in some real-world applications such as image analysis and email spam filtering, features may arrive by groups. Existing online feature selection methods evaluate features individually, while existing group feature selection methods cannot handle online processing. Motivated by this, we f...
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We study an interesting and challenging problem, online streaming feature selection, in which the size of the feature set is unknown, and not all features are available for learning while leaving the number of observations constant. In this problem, the candidate features arrive one at a time, and the learner's task is to select a “best so far” set of features from streaming features. Standard ...
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ژورنال
عنوان ژورنال: International Journal of Machine Learning and Cybernetics
سال: 2012
ISSN: 1868-8071,1868-808X
DOI: 10.1007/s13042-012-0092-x