Framework Of Rough Set Theory Research Articles

Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory

The globalization of integrated circuit (IC) design and fabrication has given rise to severe concerns with respect to modeling strategic interaction between malicious attackers and Hardware Trojan (HT) defenders using game theory. The quantitative assessment of attacker actions has made the game very challenging. In this paper, a novel rough set theory framework is proposed to analyze HT threat. The problem is formulated as an attribute weight calculation and element assessment in an information system without decision attributes. The proposed method introduces information content in the rough set that allows calculation of the weight of both core attributes and non-core attributes. For quantitative assessment, the HT threat is characterized by the closeness coefficient. In order to allow HT defenders to use fast and effective countermeasures, a threat classification method based on the k-means algorithm is proposed, and the Best Workspace Prediction (BWP) index is used to determine the number of clusters. Statistical tests were performed on the benchmark circuits in Trust-hub in order to demonstrate the effectiveness of the proposed technique for assessing HT threat. Compared with k-means, equidistant division-based k-means, and k-means++, our method shows a significant improvement in both cluster accuracy and running time.

A fixed point theorem in rough semi-linear uniform spaces

The fixed point theory has applications in various fields of science and engineering. In this paper, we introduce the concept of fixed points in the framework of rough set theory and discuss its application in the contraction of digital images. We scrutinize a rough semi-linear uniform space and prove a fixed point theorem for a rough semi-linear uniform space.

A Distributed Rough Evidential K-NN Classifier: Integrating Feature Reduction and Classification

The Evidential K-Nearest Neighbor (EK-NN) classification rule provides a global treatment of imperfect knowledge in class labels, but still suffers from the curse of dimensionality as well as runtime and memory restrictions when performing nearest neighbors search, in particular for large and high-dimensional data. To avoid the curse of dimensionality, this article first proposes a rough evidential K-NN (REK-NN) classification rule in the framework of rough set theory. Based on a reformulated K-NN rough set model, REK-NN selects features and thus reduces complexity by minimizing a proposed neighborhood pignistic decision error rate, which considers both Bayes decision error and spatial information among samples in feature space. In contrast to existing rough set-based feature selection methods, REK-NN is a synchronized rule rather than a stepwise one, in the sense that feature selection and learning are performed simultaneously. In order to further handle data with large sample size, we derive a distributed REK-NN method and implement it in the Apache Spark. The theoretical analysis of the classifier generalization error bound is finally presented. It is shown that the distributed REK-NN achieves good performances while drastically reducing the number of features and consuming less runtime and memory. Numerical experiments conducted on real-world datasets validate our conclusions.

About Granular Rough Computing—Overview of Decision System Approximation Techniques and Future Perspectives

Granular computing techniques are a huge discipline in which the basic component is to operate on groups of similar objects according to a fixed similarity measure. The first references to the granular computing can be seen in the works of Zadeh in fuzzy set theory. Granular computing allows for a very natural modelling of the world. It is very likely that the human brain, while solving problems, performs granular calculations on data collected from the senses. The researchers of this paradigm have proven the unlimited possibilities of granular computing. Among other things, they are used in the processes of classification, regression, missing values handling, for feature selection, and as mechanisms of data approximation. It is impossible to quote all methods based on granular computing—we can only discuss a selected group of techniques. In the article, we have presented a review of recently developed granulation techniques belonging to the family of approximation algorithms founded by Polkowski—in the framework of rough set theory. Starting from the basic Polkowski’s standard granulation, we have described further developed by us concept dependent, layered, and epsilon variants, and our recent homogeneous granulation. We are presenting simple numerical examples and samples of research results. The effectiveness of these methods in terms of decision system size reduction and maintenance of the internal knowledge from the original data are presented. The reduction in the number of objects in our techniques while maintaining classification efficiency reaches 90 percent—for standard granulation with usage of a kNN classifier (we achieve similar efficiency for the concept-dependent technique for the Naive Bayes classifier). The largest reduction achieved in the number of exhaustive set of rules at the efficiency level to the original data are 99 percent—it is for concept-dependent granulation. In homogeneous variants, the reduction is less than 60 percent, but the advantage of these techniques is that it is not necessary to look for optimal granulation parameters, which are selected dynamically. We also describe potential directions of development of granular computing techniques by prism of described methods.

Data meaning and knowledge discovery: Semantical aspects of information systems

Data tables provide the standard means of representation of qualitative or quantitative information about objects of interest. They also form a starting point for the task of information processing: an operation of passing from raw data or information to semantically processed knowledge. The fundamental issue here is the question about the meaning of data: What do entries in the data table actually tell us about objects? It entails another question: How should the meaning be further processed? The primary aim of the article is an attempt to answer these two questions. To this end we are going to employ conceptual scales from formal concept analysis, an important theory of data processing introduced by Rudolf Wille, and apply them to data tables so as to obtain multivalued information systems, which were introduced and developed within the conceptual framework of rough set theory by Zdzisław Pawlak and Ewa Orłowska. Our main idea is to regard multivalued information systems as the semantics or meaning of the original tables. This idea allows us to describe and combine classical rough set theory, dominance-based rough set approach, and formal concept analysis within the single framework of multivalued information systems, which is rich enough to cope with a number of semantical nuances that may occur during the process of data analysis.

Application of Rough Set-Based Feature Selection for Arabic Sentiment Analysis

Sentiment analysis is considered as one of the recent applications of text categorization that categories the emotions expressed in text as negative, positive, and natural. Rough set theory is a mathematical tool used to analyze uncertainty, incomplete information, and data reduction. Indiscernibility, reduct, and core are essential concepts in rough set theory that can be employed for data classification and knowledge reduction. This paper proposes to use the rough set-based methods for sentiment analysis to classify tweets that are written in the Arabic language. The paper investigates the application of the reduct concept of rough set theory as a feature selection method for sentiment analysis. This paper investigates four reduct computation techniques to generate the set of reducts. For classification purposes, two rule generation algorithms have been studied to build the rough set rule-based classifier. An Arabic data set of 4800 tweets is used in the experiments to validate the use of reduct computation for Arabic sentiment analysis. The results of the experiments showed that using rough set reducts techniques lead to different results and some of them can perform better than non-rough set classifier. The best classification accuracy rate was for rough set classifier using the full attribute weighting reduct generation algorithm which achieved an accuracy of 74%. The primary results indicate that using the rough set theory framework for sentiment analysis is an appealing option where it can enhance the overall accuracy and reduce the number of used terms for classification which in turn will lead to a faster classification process, especially with a large dataset.

Robust detection of moving objects in video sequences through rough set theory framework

Detection of moving objects in the presence of challenging background situations like swaying vegetation, rippling water, camera jitter etc., is known to be a difficult task. Background subtraction is considered to be better than the other approaches in terms of robustness. Its success primarily depends on the proper choice of background model(s) associated with every pixel for its foreground/background labeling. In this work, we have adopted rough-set theoretic measures to embed the spatial similarity around a neighborhood as a model for the pixel. Basic histon and its associated measure Basic Histon Roughness Index (BHRI) have been reported in the literature. It was applied to still image segmentation with impressive performance. Its adoption in video sequences for foreground/background labeling is proposed herein. We extended the histon concept to a 3D histon, which considers the intensities across the color planes in a combined manner, instead of considering independent color planes. Further, we also incorporated fuzziness into the 3D HRI measure. The labeling decision is based on Bhattacharyya distance between the model HRI and the corresponding measure in the current frame. Adoption of rough set theoretic concept into moving object segmentation is nontrivial, as the model updating requires careful consideration so that the pixels associated with gradually changing background or dynamic background are labeled as background and at the same time, slow moving objects are never adopted into the background model. A novel background model update strategy proposed herein takes these into consideration and also eliminates the need of having exclusive ideal background frame initially.

Autonomous agent control based on variable neighbourhoods

In this paper, we propose a model in which an autonomous agent selects actions based on variable neighbourhoods. We formulate relationships among variable neighbourhoods, the agent's observations, and the agent's behaviours in a framework of rough set theory and topological spaces. The main task is to explore a method by which we can select sizes of neighbourhoods under given contexts. We also show simulation results of the proposed method.

Short-Term Solar Flare Prediction Using Predictor Teams

A short-term solar flare prediction model is built using predictor teams rather than an individual set of predictors. The information provided by the set of predictors could be redundant. So it is necessary to generate subsets of predictors which can keep the information constant. These subsets are called predictor teams. In the framework of rough set theory, predictor teams are constructed from sequences of the maximum horizontal gradient, the length of neutral line and the number of singular points extracted from SOHO/MDI longitudinal magnetograms. Because of the instability of the decision tree algorithm, prediction models generated by the C4.5 decision tree for different predictor teams are diverse. The flaring sample, which is incorrectly predicted by one model, can be correctly forecasted by another one. So these base prediction models are used to construct an ensemble prediction model of solar flares by the majority voting rule. The experimental results show that the predictor team can keep the distinguishability of the original set, and the ensemble prediction model can obtain better performance than the model based on the individual set of predictors.

Study on steam turbine fault diagnosis and maintenance service grid system

It is important to ensure the safe operation, quick fault diagnosis, and maintenance of steam turbines. This is a challenging problem since steam turbines tend to be geographically dispersed and it is difficult to offer a timely response to a facility that can be a considerable distance from the maintenance division. The manufacturing grid is resource sharing technology that could provide a solution to this problem. A framework to apply this concept to steam turbine fault diagnosis and maintenance operations is proposed in this paper. In the proposed manufacturing grid the fault type is identified by an artificial neural network algorithm and the maintenance solution is given on the basis case-based reasoning with the framework of rough set theory. A prototype system is developed on the basis of proposed methodologies to demonstrate their feasibility.

Connections between rough set theory and Dempster-Shafer theory of evidence

In rough set theory there exists a pair of approximation operators, the upper and lower approximations, whereas in Dempster-Shafer theory of evidence there exists a dual pair of uncertainty measures, the plausibility and belief functions. It seems that there is some kind of natural connection between the two theories. The purpose of this paper is to establish the relationship between rough set theory and Dempster-Shafer theory of evidence. Various generalizations of the Dempster-Shafer belief structure and their induced uncertainty measures, the plausibility and belief functions, are first reviewed and examined. Generalizations of Pawlak approximation space and their induced approximation operators, the upper and lower approximations, are then summarized. Concepts of random rough sets, which include the mechanisms of numeric and non-numeric aspects of uncertain knowledge, are then proposed. Notions of the Dempster-Shafer theory of evidence within the framework of rough set theory are subsequently formed and interpreted. It is demonstrated that various belief structures are associated with various rough approximation spaces such that different dual pairs of upper and lower approximation operators induced by the rough approximation spaces may be used to interpret the corresponding dual pairs of plausibility and belief functions induced by the belief structures.

Logical aspects of learning concepts

This paper is concerned with semantic analysis of learning concepts within the framework of rough set theory. A logic is introduced to express and prove facts about relationships between extensions and intensions of concepts in an incompletely specified universe.