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 Review of Particle Swarm Optimization: Feature Selection, Classification and Hybridizations

Particle swarm optimization (PSO) is a recently grown, popular, evolutionary and conceptually simple but efficient algorithm which belongs to swarm intelligence category. This paper outlines basic concepts and reviews PSO based techniques with their applications to classification and feature selection along with some of the hybridized applications of PSO with similar other techniques. DOI: 10.17762/ijritcc2321-8169.16041

Cloud Service Provider Evaluation System using Fuzzy Rough Set Technique

Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis. However, vast diversity in available cloud service providers leads to numerous challenges for users to determine and select the best suitable service. Also, sometimes users need to hire the required services from multiple CSPs which introduce difficulties in managing interfaces, accounts, security, supports, and Service Level Agreements (SLAs). To circumvent such problems having a Cloud Service Broker (CSB) be aware of service offerings and users Quality of Service (QoS) requirements will benefit both the CSPs as well as users. In this work, we proposed a Fuzzy Rough Set based Cloud Service Brokerage Architecture, which is responsible for ranking and selecting services based on users QoS requirements, and finally monitor the service execution. We have used the fuzzy rough set technique for dimension reduction. Used weighted Euclidean distance to rank the CSPs. To prioritize user QoS request, we intended to use user assign weights, also incorporated system assigned weights to give the relative importance to QoS attributes. We compared the proposed ranking technique with an existing method based on the system response time. The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.Comment: 12 pages, 7 figures, and 8 table

Induction of accurate and interpretable fuzzy rules from preliminary crisp representation

This paper proposes a novel approach for building transparent knowledge-based systems by generating accurate and interpretable fuzzy rules. The learning mechanism reported here induces fuzzy rules via making use of only predefined fuzzy labels that reflect prescribed notations and domain expertise, thereby ensuring transparency in the knowledge model adopted for problem solving. It works by mapping every coarsely learned crisp production rule in the knowledge base onto a set of potentially useful fuzzy rules, which serves as an initial step towards an intuitive technique for similarity-based rule generalisation. This is followed by a procedure that locally selects a compact subset of the emerging fuzzy rules, so that the resulting subset collectively generalises the underlying original crisp rule. The outcome of this local procedure forms the input to a global genetic search process, which seeks for a trade-off between accuracy and complexity of the eventually induced fuzzy rule base while maintaining transparency. Systematic experimental results are provided to demonstrate that the induced fuzzy knowledge base is of high performance and interpretabilitypublishersversionPeer reviewe

Modified and Ensemble Intelligent Water Drop Algorithms and Their Applications

1.1 Introduction Optimization is a process that concerns with finding the best solution of a given problem from among the possible solutions within an affordable time and cost (Weise et al., 2009). The first step in the optimization process is formulating the optimization problem through an objective function and a set of constrains that encompass the problem search space (ie, regions of feasible solutions). Every alternative (ie, solution) is represented by a set of decision variables. Each decision variable has a domain, which is a representation of the set of all possible values that the decision variable can take. The second step in optimization starts by utilizing an optimization method (ie, search method) to find the best candidate solutions. Candidate solution has a configuration of decision variables that satisfies the set of problem constrains, and that maximizes or minimizes the objective function (Boussaid et al., 2013). It converges to the optimal solution (ie, local or global optimal solution) by reaching the optimal values of the decision variables. Figure 1.1 depicts a 3D-fitness landscape of an optimization problem. It shows the concept of the local and global optima, where the local optimal solution is not necessarily the same as the global one (Weise et al., 2009). Optimization can be applied to many real-world problems in various domains. As an example, mathematicians apply optimization methods to identify the best outcome pertaining to some mathematical functions within a range of variables (Vesterstrom and Thomsen, 2004). In the presence of conflicting criteria, engineers use optimization methods t