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IJCNN
2008
IEEE

Active Meta-Learning with Uncertainty Sampling and Outlier Detection

10 years 5 months ago
Active Meta-Learning with Uncertainty Sampling and Outlier Detection
Abstract— Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this context, i.e. each meta-example, stores the features of a given problem and information about the empirical performance obtained by the candidate algorithms on that problem. The process of constructing a set of meta-examples may be expensive, since for each problem avaliable for meta-example generation, it is necessary to perform an empirical evaluation of the candidate algorithms. Active Meta-Learning has been proposed to overcome this limitation by selecting only the most informative problems in the meta-example generation. In this work, we proposed an Active Meta-Learning method which combines Uncertainty Sampling and Outlier Detection techniques. Experiments were performed in a case study, yielding significant improvement in the MetaLearning performance.
Ricardo Bastos Cavalcante Prudêncio, Teresa
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where IJCNN
Authors Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir
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