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Analysis of Missing Data Using Matrix-Characterized Approximations

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Software Engineering Research, Management and Applications (SERA 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 845))

Abstract

Nowadays, the veracity related to data quality such as incomplete, inconsistent, vague or noisy data creates a major challenge to data mining and data analysis. Rough set theory presents a special tool for handling the incomplete and imprecise data in information systems. In this paper, rough set based matrix-represented approximations are presented to compute lower and upper approximations. The induced approximations are conducted as inputs for data analysis method, LERS (Learning from Examples based on Rough Set) used with LEM2 (Learning from Examples Module, Version2) rule induction algorithm. Analyzes are performed on missing datasets with “do not care” conditions and missing datasets with lost values. In addition, experiments on missing datasets with different missing percent by using different thresholds are also provided. The experimental results show that the system outperforms when missing data are characterized as “do not care” conditions than represented as lost values.

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Author information

Authors and Affiliations

  1. Web Mining Lab, University of Computer Studies, Mandalay, Myanmar

    Thin Thin Soe

  2. Faculty of Computer Science, University of Computer Studies, Mandalay, Myanmar

    Myat Myat Min

Authors
  1. Thin Thin Soe
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  2. Myat Myat Min
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Corresponding author

Correspondence to Thin Thin Soe .

Editor information

Editors and Affiliations

  1. Software Engineering and Information Technology Institute, Central Michigan University, Mount Pleasant, MI, USA

    Roger Lee

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Soe, T.T., Min, M.M. (2020). Analysis of Missing Data Using Matrix-Characterized Approximations. In: Lee, R. (eds) Software Engineering Research, Management and Applications. SERA 2019. Studies in Computational Intelligence, vol 845. Springer, Cham. https://doi.org/10.1007/978-3-030-24344-9_7

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