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Impact of High Dimensionality Reduction in Financial Datasets of SMEs with Feature Pre-processing in Data Mining

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Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 977))

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Abstract

High Data Dimensionality Reduction (HDDR) removes the irrelevant features in a complex dataset and incorporates various techniques that could be used to foretell the research outcomes in a predictive model. The major objective of the paper is to analyse and survey the various models on the basis of HDDR and its feature pre-processing methods applied in financial dataset predictions. Numerous techniques of data mining and its strategies were discussed and assessed to ascertain the importance of augmenting the performance of the financial dataset with classifiers. The pre-processing techniques applied in various research works and their outcomes are highlighted. The HDDR methods used in financial prediction of Small Medium Enterprises (SMEs) are studied for existing frameworks and models by different authors. The paper encapsulates the gist of the models, frameworks and algorithms involved in effective elimination of irrelevant features and extraction of best features for best prediction of financial datasets.

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Authors and Affiliations

  1. Department of Computer and Information Science, Annamalai University, Chidambaram, Tamil Nadu, India

    R. Mahalingam

  2. Department of Computer Application, Government Arts College, Chidambaram, Tamil Nadu, India

    K. Jayanthi

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  1. R. Mahalingam
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  2. K. Jayanthi
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Corresponding author

Correspondence to R. Mahalingam .

Editor information

Editors and Affiliations

  1. Department of ECE, PPG Institute of Technology, Coimbatore, Tamil Nadu, India

    V. Bindhu

  2. Faculdade de Engenharia, Departamento de, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. San Jose State University, FREMONT, CA, USA

    Chandrasekar Vuppalapati

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Mahalingam, R., Jayanthi, K. (2023). Impact of High Dimensionality Reduction in Financial Datasets of SMEs with Feature Pre-processing in Data Mining. In: Bindhu, V., Tavares, J.M.R.S., Vuppalapati, C. (eds) Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 977. Springer, Singapore. https://doi.org/10.1007/978-981-19-7753-4_29

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  • DOI: https://doi.org/10.1007/978-981-19-7753-4_29

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