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Unsupervised Feature Selection Using Correlation Score

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Book cover Computing, Communication and Signal Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 810))

Abstract

Data of huge dimensionality is generated because of wide application of technologies. Using this data for the very purpose of decision-making is greatly affected because of the curse of dimensionality as selection of all features will lead to overfitting and ignoring the relevant ones can lead to information loss. Feature selection algorithms help to overcome this problem by identifying the subset of original features by retaining relevant features and by removing the redundant ones. This paper aims to evaluate and analyze some of the most popular feature selection algorithms using different benchmarked datasets. Relief, ReliefF, and Random Forest algorithms are evaluated and analyzed in the form of combinations of different rankers and classifiers. It is observed empirically that the accuracy of the ranker and classifier varies from dataset to dataset. This paper introduces the concept of applying multivariate correlation analysis (MCA) for feature selection. From results, it can be inferred that MCA exhibits better performance over the legacy-based feature selection algorithms.

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Author information

Authors and Affiliations

  1. College of Engineering Pune, Savitribai Phule Pune University, Pune, India

    Tanuja Pattanshetti & Vahida Attar

Authors
  1. Tanuja Pattanshetti
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  2. Vahida Attar
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Corresponding author

Correspondence to Tanuja Pattanshetti .

Editor information

Editors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, Maharashtra, India

    Brijesh Iyer

  2. Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, Maharashtra, India

    S.L. Nalbalwar

  3. Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Nagendra Prasad Pathak

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Pattanshetti, T., Attar, V. (2019). Unsupervised Feature Selection Using Correlation Score. In: Iyer, B., Nalbalwar, S., Pathak, N. (eds) Computing, Communication and Signal Processing . Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-13-1513-8_37

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  • DOI: https://doi.org/10.1007/978-981-13-1513-8_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1512-1

  • Online ISBN: 978-981-13-1513-8

  • eBook Packages: EngineeringEngineering (R0)

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