Avoiding anomalies in data stream learning

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

João Gama (Author) FEP; Petr Kosina (Author) Other; Ezilda Almeida (Author) Other

Conference proceedings International

Title: DISCOVERY SCIENCE

Pages: 49-63

16th International Conference on Discovery Science (DS)

Singapore, SINGAPORE, OCT 06-09, 2013

Indexing

ISI Proceedings

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Scientific classification

FOS: Engineering and technology

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-008-HMP

DOI: 10.1007/978-3-642-40897-7_4

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