Abstract
Dimensionality reduction is the most popular technique, which is used in data analytics for the optimal solution over high-dimensional data. Many times some inconsistency and uncertainty are presented in huge data so to deal with such a type of data soft set theory is being developed. Soft set theory handles the difficulties of older theories like fuzzy set, rough set using new property that is parameterization reduction. This paper presents different soft set based NPR algorithms for information systems that give optimal solutions for big data. The goal of this article is to focus on the dimensionality reduction approach of data mining using soft set theory which can be applied to machine learning field. Thus normal dimensionality reduction of soft set (NDRSS) algorithm is proposed for real-time data to give a better optimal solution than existing soft set algorithms. The result shows that how-high dimensional data is to be reduced using soft set theory in machine learning.
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Lanjewar, P.D., Momin, B.F. (2022). Application of Soft Set Theory for Dimensionality Reduction Approach in Machine Learning. In: Srivastava, P., Thakur, S.S., Oros, G.I., AlJarrah, A.A., Laohakosol, V. (eds) Mathematical, Computational Intelligence and Engineering Approaches for Tourism, Agriculture and Healthcare . Lecture Notes in Networks and Systems, vol 214. Springer, Singapore. https://doi.org/10.1007/978-981-16-3807-7_17
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DOI: https://doi.org/10.1007/978-981-16-3807-7_17
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