Avoiding anomalies in data stream learning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

João Gama (Autor) FEP; Petr Kosina (Autor) Outra; Ezilda Almeida (Autor) Outra

Ata de Conferência Internacional

Título: DISCOVERY SCIENCE

Páginas: 49-63

16th International Conference on Discovery Science (DS)

Singapore, SINGAPORE, OCT 06-09, 2013

Indexação

ISI Proceedings

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias

CORDIS: Ciências Físicas > Ciência de computadores

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15