Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey

Abstract

Data used in machine learning applications is prone to contain both vague and incomplete information. Many authors have proposed to use fuzzy rough set theory in the development of new techniques tackling these characteristics. Fuzzy sets deal with vague data, while rough sets allow to model incomplete information. As such, the hybrid setting of the two paradigms is an ideal candidate tool to confront the separate challenges. In this paper, we present a thorough review on the use of fuzzy rough sets in machine learning applications. We recall their integration in preprocessing methods and consider learning algorithms in the supervised, unsupervised and semi-supervised domains and outline future challenges. Throughout the paper, we highlight the interaction between theoretical advances on fuzzy rough sets and practical machine learning tools that take advantage of them.

Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

“…The fuzzy rough set theory is a hybrid theory, which encapsulates fuzziness and roughness into a single model. It studies the operators that approximate fuzzy sets with fuzzy relations (Vluymans et al, 2015). Thus, the discretization requirement of the rough set theory is removed by computing the similarity between instances with fuzzy relations (Ma et al, 2018; Wang et al, 2018; Wang, Ji, & Song, 2018; Zhao & Zhang, 2011).…”

Double Successive Rough Set Approximations

“…Therefore, rough set analysis is added to the tools, which includes regression analysis and Bayes' Theorem, for pattern recognition and feature selection in data mining, see [19,36,10,37,29,5,26,28]. The resulting applications include in medical databases [32,31,30,8,9,12,13], cognitive science [16,14,25,17,35], artificial intelligence and machine learning [15,11,7,6,33,21,27] and engineering [1,2,3,24,23]. Indeed in [34], Yao noted that there is currently an imbalance in the literature between the conceptual unfolding of rough set theory and its practical computational progress.…”

Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

“…The concept of rough sets was extended by analyzing the properties of lower and upper approximations of fuzzy sets [27][28][29][30][31] with corresponding practicable applications. For example, in the significant field of machine learning [32] or even medical image analyses, as an image segmentation approach [33].…”