Probabilistic variable precision fuzzy rough set technique for discovering optimal learning patterns in e-learning
by K.S. Bhuvaneshwari; D. Bhanu; S. Sophia; S. Kannimuthu
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 14, No. 1/2, 2019

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.

Online publication date: Tue, 11-Dec-2018

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