Rough Set Approach in Machine Learning: A Review

  • Mahajan P
  • Kandwal R
  • Vijay R
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Abstract

Most Read Research Articles Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics An Effective Evolutionary Clustering Algorithm: Hepatitis C Case Study Migration of Legacy Information System based on Business Process Theory Research on Care of Postoperative Patient based on Rough Sets Theory Using Box Approach in Persian Handwritten Digits Recognition HomeArchivesVolume 56Number 10Rough Set Approach in Machine Learning: A Review Call for Paper - April 2013 Edition IJCA solicits original research papers for the April 2013 Edition of IJCA. Last date of manuscript submission is March 20, 2013. Read More Rough Set Approach in Machine Learning: A Review inShare International Journal of Computer Applications © 2012 by IJCA Journal Volume 56 - Number 10 Year of Publication: 2012 Authors: Prerna MahajanRekha KandwalRitu Vijay 10.5120/8924-2996 Prerna Mahajan, Rekha Kandwal and Ritu Vijay. Article: Rough Set Approach in Machine Learning: A Review. International Journal of Computer Applications 56(10):1-13, October 2012. Published by Foundation of Computer Science, New York, USA. BibTeX Abstract The Rough Set (RS) theory can be considered as a tool to reduce the input dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there has been a rapid growth in interest in rough set theory and its applications in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, data preprocessing, knowledge discovery, decision analysis, and expert systems. This paper discusses the basic concepts of rough set theory and point out some rough set-based research directions and applications. The discussion also includes a review of rough set theory in various machine learning techniques like clustering, feature selection and rule induction.

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APA

Mahajan, P., Kandwal, R., & Vijay, R. (2012). Rough Set Approach in Machine Learning: A Review. International Journal of Computer Applications, 56(10), 1–13. https://doi.org/10.5120/8924-2996

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