Title: Probabilistic variable precision fuzzy rough set technique for discovering optimal learning patterns in e-learning

Authors: K.S. Bhuvaneshwari; D. Bhanu; S. Sophia; S. Kannimuthu

Addresses: Department of CSE, Karpagam College of Engineering, Coimbatore 641032,Tamil Nadu, India ' Department of CSE, Karpagam Institute of Technology, Coimbatore, 641105, Tamil Nadu, India ' Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore 641008, Tamil Nadu, India ' Department of CSE, Karpagam College of Engineering, Coimbatore 641032, Tamil Nadu, India

Abstract: In e-learning environment, optimal learning patterns are discovered for realising and understanding the effective learning styles. The value of uncertain and imprecise knowledge collected has to be categorised into classes known as membership grades. Rough set theory is potential in categorising data into equivalent classes and fuzzy logic may be applied through soft thresholds for refining equivalence relation that quantifies correlation between each class of elucidated data. In this paper, probabilistic variable precision fuzzy rough set technique (PVPFRST) is proposed for deriving robust approximations and generalisations that handles the types of uncertainty namely stochastic, imprecision and noise in membership functions. The result infers that the degree of accuracy of PVPFRST is 21% superior to benchmark techniques. Result proves that PVPFRST improves effectiveness and efficiency in identifying e-learners styles and increases the performance by 27%, 22% and 25% in terms of discrimination rate, precision and recall value than the benchmark approaches.

Keywords: inclusion degree; probabilistic fuzzy information system; fuzzy membership grade; crispness coefficient; probabilistic variable precision fuzzy rough set; inclusion function.

DOI: 10.1504/IJBIDM.2019.096807

International Journal of Business Intelligence and Data Mining, 2019 Vol.14 No.1/2, pp.121 - 137

Available online: 16 Nov 2018 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article