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- W3137802172 abstract "Several data mining tasks and techniques are available to extract hidden knowledge from large databases. Classification is one such task widely used to categorize different input data into target classes. A myriad of machine learning algorithms have been proposed for the purpose. Meta-learning combines multiple base classifiers whose individual performances in some way contribute to the overall classification. There has been significant interest in combining machine learning algorithms to solve many challenging real-world problems. The premise is that meta-learning enhances the data mining task with the ability to learn and adapt from previous experience. In this paper, we investigate the performance of two popular meta-learning approaches, considering several well-known learning algorithms on a variety of public datasets." @default.
- W3137802172 created "2021-03-29" @default.
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- W3137802172 date "2015-10-07" @default.
- W3137802172 modified "2023-09-23" @default.
- W3137802172 title "Meta-Learning in Data Classification: An Analysis" @default.
- W3137802172 hasPublicationYear "2015" @default.
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