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@inproceedings{1128372,
author = {Gama, Joao and Kosina, Petr and Almeida, Ezilda},
address = {Berlin Heidelberg},
booktitle = {Discovery Science, Proceedings of 16th International Conference DS 2013},
doi = {http://dx.doi.org/10.1007/978-3-642-40897-7_4},
editor = {Johannes Furnkranz, Eyke Hullermeier,Tomoyuki Higuchi},
keywords = {Data Streams; Rule Learning; Anomaly Detection},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Berlin Heidelberg},
isbn = {978-3-642-40896-0},
pages = {49-63},
publisher = {Springer},
title = {Avoiding Anomalies in Data Stream Learning},
url = {http://link.springer.com/chapter/10.1007%2F978-3-642-40897-7_4},
year = {2013}
}
TY - JOUR
ID - 1128372
AU - Gama, Joao - Kosina, Petr - Almeida, Ezilda
PY - 2013
TI - Avoiding Anomalies in Data Stream Learning
PB - Springer
CY - Berlin Heidelberg
SN - 9783642408960
KW - Data Streams
KW - Rule Learning
KW - Anomaly Detection
UR - http://link.springer.com/chapter/10.1007%2F978-3-642-40897-7_4
L2 - http://link.springer.com/chapter/10.1007%2F978-3-642-40897-7_4
N2 - 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.
ER -
GAMA, Joao, Petr KOSINA and Ezilda ALMEIDA. Avoiding Anomalies in Data Stream Learning. Online. In Johannes Furnkranz, Eyke Hullermeier,Tomoyuki Higuchi. \textit{Discovery Science, Proceedings of 16th International Conference DS 2013}. Berlin Heidelberg: Springer, 2013. p.~49-63. ISBN~978-3-642-40896-0. Available from: https://dx.doi.org/10.1007/978-3-642-40897-7\_{}4. [citováno 2024-04-23]
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