Showing papers on "Rough set published in 2014"
TL;DR: The objective of this study is to develop a new multigranulation rough set model, called a multigsranulation decision-theoretic rough set, which can interprete the parameters from existing forms of probabilistic approaches to rough sets.
315 citations
TL;DR: New supervised feature selection methods based on hybridization of Particle Swarm Optimization, PSO based Relative Reduct andPSO based Quick Reduct are presented for the diseases diagnosis, proving the efficiency of the proposed technique as well as enhancements over the existing feature selection techniques.
267 citations
TL;DR: This work introduces incremental mechanisms for three representative information entropies and develops a group incremental rough feature selection algorithm based on information entropy that aims to find the new feature subset in a much shorter time when multiple objects are added to a decision table.
Abstract: Many real data increase dynamically in size. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a decision table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy. When multiple objects are added to a decision table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient.
264 citations
TL;DR: Novel dynamic two- way decisions and dynamic three-way decisions based on three-Way decisions spaces and three- Way decisions with a pair of evaluation functions are introduced.
202 citations
TL;DR: A novel FMEA approach for obtaining a more rational rank of failure modes is proposed, which integrates the strength of rough set theory in handling vagueness and the merit of TOPSIS in modeling multi-criteria decision making.
Abstract: This study aims at improving the effectiveness of failure mode and effect analysis (FMEA) technique. FMEA is a widely used technique for identifying and eliminating known or potential failures from system, design, and process. However, in conventional FMEA, risk factors of Severity (S), Occurrence (O), and Detection difficulty (D) are simply multiplied to obtain a crisp risk priority number without considering the subjectivity and vagueness in decision makers’ judgments. Besides, the weights for risk factors S, O, and D are also ignored. As a result, the effectiveness and accuracy of the FMEA are affected. To solve this problem, a novel FMEA approach for obtaining a more rational rank of failure modes is proposed. Basically, two stages of evaluation process are described: the determination of risk factors’ weights and ranking the risk for the failure modes. A rough group ‘Technique for Order Performance by Similarity to Ideal Solution’ (TOPSIS) method is used to evaluate the risk of failure mode. The novel approach integrates the strength of rough set theory in handling vagueness and the merit of TOPSIS in modeling multi-criteria decision making. Finally, an application in steam valve system is provided to demonstrate the potential of the methodology under vague and subjective environment. Copyright © 2013 John Wiley & Sons, Ltd.
188 citations
TL;DR: The empirical studies of corporate failure prediction and high school program choices' prediction validate the rationality and effectiveness of the proposed approach to provide a new classification approach.
172 citations
TL;DR: An extended rough set model, called as composite rough sets, is presented, and a novel matrix-based method for fast updating approximations is proposed in dynamic composite information systems.
160 citations
TL;DR: Granular models as discussed by the authors are generalizations of numeric models that are formed as a result of an optimal allocation (distribution) of information granularity, which helps establish a better rapport of the resulting granular model with the system under modeling.
159 citations
TL;DR: A new multigranulation rough set based decision model based on SCED strategy, called pessimistic multigramulation rough sets is developed, which is studied from three aspects, which are lower/upper approximation and their properties, decision rules and attribute reduction.
146 citations
TL;DR: Experimental results on text categorization suggest that the overall uncertainty of probabilistic regions may be reduced with the threshold configuration mechanism.
142 citations
TL;DR: An optimization method for three-way decisions with IVDTRS is proposed, which is designed to minimize the overall uncertainty based on the Shannon entropy and can support decision making in the uncertain environment.
TL;DR: The package RoughSets, written mainly in the R language, provides implementations of methods from the rough set theory and fuzzy rough set theories for data modeling and analysis and should be considered as an alternative software library for analyzing data based on RST and FRST.
TL;DR: An optimization representation of decision-theoretic rough set model is proposed by considering the minimization of the decision cost and two significant inferences can be drawn from the solution of the optimization problem.
TL;DR: This study develops a new multigranulation rough set model, called an intuitionistic fuzzy multigraphic rough set (IFMGRS), that is generalizations ofThree types of IFMGRSs are proposed that are extensions of three existing intuitionism fuzzy rough sets.
TL;DR: This paper develops a hybrid structure called rough neutrosophic sets and studied their properties.
Abstract: Both neutrosophic sets theory and rough sets theory are emerging as power- ful tool for managing uncertainty, indeterminate, incomplete and imprecise information. In this paper we develop an hybrid structure called rough neutrosophic sets and studied their properties.
TL;DR: Experiments on six microarray data sets show that the fast algorithm can effectively reduce the computational time in comparison with the naive algorithm when facing high dimensional data sets, and it is shown that fast algorithm is useful in decreasing the computationalTime of finding both traditional reduct and attribute clustering based reduct.
Abstract: Dynamic updating of the rough approximations is a critical factor for the success of the rough set theory since data is growing at an unprecedented rate in the information-explosion era. Though many updating schemes have been proposed to study such problem, few of them were carried out in a multigranulation environment. To fill such gap, the updating of the multigranulation rough approximations is firstly explored in this paper. Both naive and fast algorithms are presented for updating the multigranulation rough approximations with the increasing of the granular structures. Different from the naive algorithm, the fast algorithm is designed based on the monotonic property of the multigranulation rough approximations. Experiments on six microarray data sets show us that the fast algorithm can effectively reduce the computational time in comparison with the naive algorithm when facing high dimensional data sets. Moreover, it is also shown that fast algorithm is useful in decreasing the computational time of finding both traditional reduct and attribute clustering based reduct.
06 Jul 2014
TL;DR: Three kinds of covering generalized rough sets for dealing with the vagueness and granularity in information systems are studied and the relationships among these three types of covering rough sets are explored.
Abstract: The study of definable sets in various generalized rough set models would provide better understanding to these models. Some algebraic structures of all definable sets have been investigated, and the relationships among the definable sets, the inner definable sets and the outer definable sets have been presented. In this paper, we further study the definable sets in three types of covering-based rough sets and present several necessary and sufficient conditions of definable sets. These three types of covering-based rough sets are based on three kinds of neighborhoods: the neighborhood, the complementary neighborhood and the indiscernible neighborhood, respectively. Some necessary and sufficient conditions of definable sets are presented through these three types of neighborhoods, and the relationships among the definable sets are investigated. Moreover, we study the relationships among these three types of neighborhoods, and present certain conditions that the union of the neighborhood and the complementary neighborhood is equal to the indiscernible neighborhood.
TL;DR: It is found that for any subset X ⊆ U, the lower approximations of X and the upper approximation of X under the four types of MGCRS models can construct a lattice, if the authors consider the binary relation of inclusion.
TL;DR: This work proposes an incremental approach for feature selection, which can accelerate the feature selection process in dynamic incomplete data and shows that the proposed algorithms compare favorably with that of applying the existing non-incremental methods.
TL;DR: The main contribution of this paper is to extend the probabilistic rough set to fuzzy environment, i.e., the probablistic rough fuzzy set model to be presented by using the process of decision-making under conditions of risk.
TL;DR: The proposed attribute reduction deals with heterogeneous condition attributes from the viewpoint of discernible ability and can consider the mutual effects between two types of attributes without preprocessing into single-typed ones.
Abstract: Attribute reduction with rough sets aims to delete superfluous condition attributes from a decision system by considering the inconsistency between condition attributes and the decision labels. However, heterogeneous condition attributes including symbolic and real-valued ones always coexist for most decision systems and different types of attributes induce different kinds of granular structures. The existing rough set models do not have explicit mechanisms to address different kinds of granular structures reasonably and effectively. In this paper, we aim to perform attribute reduction for decision systems with symbolic and real-valued condition attributes by composing classical rough set and fuzzy rough set models. We first define a discernibility relation for every symbolic and real-valued condition attribute to characterize its discernible ability related to the decision labels. With these discernibility relations, we can develop a dependence function to measure the inconsistency between heterogeneous condition attributes and decision labels, and attribute reduction aims to keep this dependence function with a small perturbation. The proposed attribute reduction deals with heterogeneous condition attributes from the viewpoint of discernible ability and can consider the mutual effects between two types of attributes without preprocessing into single-typed ones. An algorithm to find reducts is developed and experiments are performed to demonstrate that the proposed idea is effective.
31 Dec 2014
TL;DR: This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.
Abstract: This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.
TL;DR: This paper provides a new formulation of multi-class decision-theoretic rough sets with a third option of making a deferment decision added to each class to give users the flexibility of further examining the suspicious objects, thereby reducing the chance of misclassification.
TL;DR: The experimental results for UCI data sets show that the proposed reduction approach is an effective technique for addressing numerical and categorical data and is more efficient than the method presented in the paper.
TL;DR: The efficiency of this quick reduct algorithm based on neighborhood rough set model is proved by comparable experiments, and especially this algorithm is more suitable for the reduction of big data.
TL;DR: Two novel multi-label classification algorithms based on the variable precision neighborhood rough sets, called MLRS and MLRS-LC, achieve promising performance when compared with some well-known multi- label learning algorithms.
Abstract: Nowadays, multi-label classification methods are of increasing interest in the areas such as text categorization, image annotation and protein function classification. Due to the correlation among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents two novel multi-label classification algorithms based on the variable precision neighborhood rough sets, called multi-label classification using rough sets (MLRS) and MLRS using local correlation (MLRS-LC). The proposed algorithms consider two important factors that affect the accuracy of prediction, namely the correlation among the labels and the uncertainty that exists within the mapping between the feature space and the label space. MLRS provides a global view at the label correlation while MLRS-LC deals with the label correlation at the local level. Given a new instance, MLRS determines its location and then computes the probabilities of labels according to its location. The MLRS-LC first finds out its topic and then the probabilities of new instance belonging to each class is calculated in related topic. A series of experiments reported for seven multi-label datasets show that MLRS and MLRS-LC achieve promising performance when compared with some well-known multi-label learning algorithms.
TL;DR: This paper proposes incremental approaches for updating approximations dynamically in set-valued ordered decision systems under the attribute generalization, which involves several modifications to relevant matrices without having to retrain from the start on all accumulated training data.
TL;DR: Three new monotonic measures are constructed by considering variants of the conditional information entropy, from which they can obtain the heuristic reduction algorithms and validate the monotonicity of new measures and verify the effectiveness of decision region distribution preservation reducts.
TL;DR: A framework of quantitative rough sets based on subsethood measures is proposed, which enables us to classify and unify existing generalized rough set models, to investigate limitations of existing models, and to develop new models.
TL;DR: To mine knowledge from big data, parallel large-scale rough set based methods for knowledge acquisition using MapReduce are presented and it is demonstrated that the proposed parallel methods can effectively process very large data on different runtime systems.