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The WEKA data mining software: an update
More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text onExpand
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Classifier chains for multi-label classification
The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceivedExpand
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MOA: Massive Online Analysis
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline andExpand
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  • Open Access
New ensemble methods for evolving data streams
Advanced analysis of data streams is quickly becoming a key area of data mining research as the number of applications demanding such processing increases. Online mining when such data streams evolveExpand
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Active Learning With Drifting Streaming Data
In learning to classify streaming data, obtaining true labels may require major effort and may incur excessive cost. Active learning focuses on carefully selecting as few labeled instances asExpand
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Leveraging Bagging for Evolving Data Streams
Bagging, boosting and Random Forests are classical ensemble methods used to improve the performance of single classifiers. They obtain superior performance by increasing the accuracy and diversity ofExpand
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  • Open Access
Multi-label Classification Using Ensembles of Pruned Sets
This paper presents a pruned sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process toExpand
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Locally Weighted Naive Bayes
Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results,Expand
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  • Open Access
Multinomial Naive Bayes for Text Categorization Revisited
This paper presents empirical results for several versions of the multinomial naive Bayes classifier on four text categorization problems, and a way of improving it using locally weighted learningExpand
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Adaptive random forests for evolving data stream classification
Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demandsExpand
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