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Description
  • 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 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. (en)
Title
  • Avoiding Anomalies in Data Stream Learning
  • Avoiding Anomalies in Data Stream Learning (en)
skos:prefLabel
  • Avoiding Anomalies in Data Stream Learning
  • Avoiding Anomalies in Data Stream Learning (en)
skos:notation
  • RIV/00216224:14330/13:00070032!RIV14-MSM-14330___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(LG13010), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 62752
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/13:00070032
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Data Streams; Rule Learning; Anomaly Detection (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [9D9957BF1D45]
http://linked.open...v/mistoKonaniAkce
  • Singapore
http://linked.open...i/riv/mistoVydani
  • Berlin Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Discovery Science, Proceedings of 16th International Conference DS 2013
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kosina, Petr
  • Gama, Joao
  • Almeida, Ezilda
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-40897-7_4
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 9783642408960
http://localhost/t...ganizacniJednotka
  • 14330
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