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research-article

Handling data irregularities in classification: : Foundations, trends, and future challenges

Authors:

Bidyut B. ChaudhuriAuthors Info & Claims

Volume 81, Issue C

Pages 674 - 693

https://doi.org/10.1016/j.patcog.2018.03.008

Published: 01 September 2018 Publication History

Highlights

•

Data irregularities can significantly degrade the performance of classifiers.

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We present a comprehensive taxonomy and survey of various data irregularities.

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We discuss prominent methods to handle distribution and feature-based irregularities.

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We highlight the co-occurrences and interrelations among different irregularities.

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We unearth a number of promising future research avenues.

Abstract

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms of similar underlying class distributions, balanced size of classes, the presence of a full set of observed features in all data instances, etc. Practical datasets, however, show up with various forms of irregularities that are, very often, sufficient to confuse a classifier, thus degrading its ability to learn from the data. In this article, we provide a bird’s eye view of such data irregularities, beginning with a taxonomy and characterization of various distribution-based and feature-based irregularities. Subsequently, we discuss the notable and recent approaches that have been taken to make the existing stand-alone as well as ensemble classifiers robust against such irregularities. We also discuss the interrelation and co-occurrences of the data irregularities including class imbalance, small disjuncts, class skew, missing features, and absent (non-existing or undefined) features. Finally, we uncover a number of interesting future research avenues that are equally contextual with respect to the regular as well as deep machine learning paradigms.

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Index Terms

Handling data irregularities in classification: Foundations, trends, and future challenges
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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Pattern Recognition Volume 81, Issue C

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Sharman RAgrawal LMulgund P(2024)Handling Imbalanced Data With Weighted Logistic Regression and Propensity Score Matching methodsJournal of Database Management10.4018/JDM.33588835:1(1-37)Online publication date: 7-Jan-2024
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Sun ZYing WZhang WGong S(2024)Undersampling method based on minority class density for imbalanced dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123328249:PAOnline publication date: 1-Sep-2024
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Zoghi ZSerpen G(2024)UNSW‐NB15 computer security datasetSecurity and Privacy10.1002/spy2.3317:1Online publication date: 9-Jan-2024
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