A methodology for enhancing data quality for classification purposes using attribute-based decision graphs
João Roberto Bertini Junior
ARTIGO
Inglês
Agradecimentos: The author would like to thank FAEPEX and FAPESP for the financial support
Abstract: The accuracy performance of a classification system strongly depends on the quality of the data used to train it. Among other issues, noise in the attribute values degrades data quality and interferes badly with the process of automatic classification. This paper proposes a novel method of...
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Abstract: The accuracy performance of a classification system strongly depends on the quality of the data used to train it. Among other issues, noise in the attribute values degrades data quality and interferes badly with the process of automatic classification. This paper proposes a novel method of data cleansing designed for enhancing classification accuracy. The cleansing procedure is based on the Attribute-based Decision Graphs, which are graphs built over the attribute space of a data set. Such graphs gather the underlying patterns from the data set and use this knowledge to check each attribute value for noise. Classification results considering four learning algorithms and five data sets with artificially added noise have shown the effectiveness of the proposed cleansing procedure
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FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP
Fechado
DOI: https://doi.org/10.1109/LA-CCI.2017.8285692
Texto completo: https://ieeexplore.ieee.org/document/8285692
A methodology for enhancing data quality for classification purposes using attribute-based decision graphs
João Roberto Bertini Junior
A methodology for enhancing data quality for classification purposes using attribute-based decision graphs
João Roberto Bertini Junior
Fontes
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Proceedings of the 4th IEEE Latin American Conference on Computational Intelligence Piscataway, NJ : Institute of Electrical and Electronics Engineers, 2017. |