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Avoiding anomalies in data stream learning

Title
Avoiding anomalies in data stream learning
Type
Article in International Conference Proceedings Book
Year
2013
Authors
João Gama
(Author)
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Petr Kosina
(Author)
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Ezilda Almeida
(Author)
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Conference proceedings International
Pages: 49-63
16th International Conference on Discovery Science (DS)
Singapore, SINGAPORE, OCT 06-09, 2013
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Publicação em ISI Proceedings ISI Proceedings
Publicação em ISI Web of Knowledge ISI Web of Knowledge - 0 Citations
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Scientific classification
FOS: Engineering and technology
CORDIS: Physical sciences > Computer science
Other information
Authenticus ID: P-008-HMP
Abstract (EN): The presence of anomalies in data compromises data quality and can reduce the effectiveness of learning algorithms. Standard data mining methodologies refer to data cleaning as a pre-processing before the learning task. The problem of data cleaning is exacerbated when learning in the computational model of data streams. In this paper we present a streaming algorithm for learning classification rules able to detect contextual anomalies in the data. Contextual anomalies are surprising attribute values in the context defined by the conditional part of the rule. For each example we compute the degree of anomaliness based on the probability of the attribute-values given the conditional part of the rule covering the example. The examples with high degree of anomaliness are signaled to the user and not used to train the classifier. The experimental evaluation in real-world data sets shows the ability to discover anomalous examples in the data. The main advantage of the proposed method is the ability to inform the context and explain why the anomaly occurs.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 15
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