Rough sets, their extensions and applications

Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Despite its recency, the theory and its extensions have been widely applied to many problems, including decision analysis, data-mining, intelligent control and pattern recognition. This paper presents an outline of the basic concepts of rough sets and their major extensions, covering variable precision, tolerance and fuzzy rough sets. It also shows the diversity of successful applications these theories have entailed, ranging from financial and business, through biological and medicine, to physical, art, and meteorological

A Noise-tolerant Approach to Fuzzy-Rough Feature Selection

In rough set based feature selection, the goal is to omit attributes (features) from decision systems such that objects in different decision classes can still be discerned. A popular way to evaluate attribute subsets with respect to this criterion is based on the notion of dependency degree. In the standard approach, attributes are expected to be qualitative; in the presence of quantitative attributes, the methodology can be generalized using fuzzy rough sets, to handle gradual (in)discernibility between attribute values more naturally. However, both the extended approach, as well as its crisp counterpart, exhibit a strong sensitivity to noise: a change in a single object may significantly influence the outcome of the reduction procedure. Therefore, in this paper, we consider a more flexible methodology based on the recently introduced Vaguely Quantified Rough Set (VQRS) model. The method can handle both crisp (discrete-valued) and fuzzy (real-valued) data, and encapsulates the existing noise-tolerant data reduction approach using Variable Precision Rough Sets (VPRS), as well as the traditional rough set model, as special cases

Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches

Abstract—Semantics-preserving dimensionality reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition, and signal processing. This has found successful application in tasks that involve data sets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and Web content classification. One of the many successful applications of rough set theory has been to this feature selection area. This paper reviews those techniques that preserve the underlying semantics of the data, using crisp and fuzzy rough set-based methodologies. Several approaches to feature selection based on rough set theory are experimentally compared. Additionally, a new area in feature selection, feature grouping, is highlighted and a rough set-based feature grouping technique is detailed. Index Terms—Dimensionality reduction, feature selection, feature transformation, rough selection, fuzzy-rough selection.

An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network)