Showing papers on "Rough set published in 2019"
TL;DR: A general steganalysis feature selection method based on decision rough set-positive region reduction that can significantly reduce the feature dimensions and maintain detection accuracy and will remarkably improve the efficiency of feature extraction and stego image detection.
Abstract: Steganography detection based on Rich Model features is a hot research direction in steganalysis. However, rich model features usually result a large computation cost. To reduce the dimension of steganalysis features and improve the efficiency of steganalysis algorithm, differing from previous works that normally proposed new feature extraction algorithm, this paper proposes a general steganalysis feature selection method based on decision rough set $\alpha$ -positive region reduction. First, it is pointed out that decision rough set $\alpha$ -positive region reduction is suitable for steganalysis feature selection. Second, a quantization method of attribute separability is proposed to measure the separability of steganalysis feature components. Third, steganalysis feature components selection algorithm based on decision rough set $\alpha$ -positive region reduction is given; thus, stego images can be detected by the selected feature. The proposed method can significantly reduce the feature dimensions and maintain detection accuracy. Based on the BOSSbase-1.01 image database of 10 000 images, a series of feature selection experiments are carried on two kinds of typical rich model features (35263-D J+SRM feature and 17000-D GFR feature). The results show that even though these two kinds of features are reduced to approximately 8000-D, the detection performance of steganalysis algorithms based on the selected features are also maintained with that of original features, which will remarkably improve the efficiency of feature extraction and stego image detection.
171 citations
TL;DR: A heuristic feature selection algorithm with low computational complexity is presented to improve the performance of cancer classification using gene expression data and outperforms other related methods in terms of the number of selected genes and the classification accuracy, especially as the size of the genes increases.
153 citations
TL;DR: A novel type of soft rough covering is introduced by means of soft neighborhoods, and then it is used to improve decision making in a multicriteria group environment.
Abstract: In this paper, we contribute to a recent and successful modelization of uncertainty, which the practitioner often encounters in the formulation of multicriteria group decision making problems. To be precise, in order to approach the uncertainty issue we introduce a novel type of soft rough covering by means of soft neighborhoods, and then we use it to improve decision making in a multicriteria group environment. Our research method is as follows. Firstly we introduce the soft covering upper and lower approximation operators of soft rough coverings. Then its relationships with well-established types of soft rough coverings are analyzed. Secondly, we define and investigate the measure degree of our novel soft rough covering. With this tool we produce a new class of soft rough sets. Finally, we propose an application of such soft rough covering model to multicriteria group decision making by means of an algorithmic solution. A fully developed example supports the implementability of this decision making method.
147 citations
TL;DR: Two novel decision-making methods that are stated in terms of novel and flexible generalized IF rough set models are set forth in order to solve MADM problems with the evaluation of IF information based on covering-based generalized IFrough set models.
133 citations
01 Aug 2019
TL;DR: Two incremental hill-climbing techniques are hybridized with the binary ant lion optimizer in a model called HBALO, which shows the superior performance of the proposed approaches in searching the feature space for optimal feature combinations.
Abstract: Feature selection (FS) can be defined as the problem of finding the minimal number of features from an original set with the minimum information loss. Since FS problems are known as NP-hard problems, it is necessary to investigate a fast and an effective search algorithm to tackle this problem. In this paper, two incremental hill-climbing techniques (QuickReduct and CEBARKCC) are hybridized with the binary ant lion optimizer in a model called HBALO. In the proposed approach, a pool of solutions (ants) is generated randomly and then enhanced by embedding the most informative features in the dataset that are selected by the two filter feature selection models. The resultant population is then used by BALO algorithm to find the best solution. The proposed binary approaches are tested on a set of 18 well-known datasets from UCI repository and compared with the most recent related approaches. The experimental results show the superior performance of the proposed approaches in searching the feature space for optimal feature combinations.
131 citations
TL;DR: In this article, the authors proposed two methods that benefit from some novel fuzzy rough set models for multi-attribute decision-making problems, which can be used to deal with misclassification and perturbation.
Abstract: At present, there is no unified method for solving multiattribute decision-making problems. In this paper, we propose two methods that benefit from some novel fuzzy rough set models. Some theoretical preliminaries pave the way. First, by means of a fuzzy logical implicator $\mathcal {I}$ and a triangular norm $\mathcal {T}$ , four types of coverings-based variable precision $(\mathcal {I},\mathcal {T})$ -fuzzy rough set models are proposed. They can be used to deal with misclassification and perturbation (here, misclassification refers to error or missing values in classification, while perturbation refers to small changes in digital data). Second, the properties and the relationships among these models are investigated. Finally, we rely on their remarkable features in order to establish two approaches to multiattribute decision-making. Some numerical examples illustrate the application of these new approaches. The sensitivity and comparative analyses show that the respective ranking results produced by these decision-making methods have a high consensus for multiattribute decision-making problems with fuzzy evaluation information.
115 citations
TL;DR: A new neighborhood rough set model called k-nearest neighborhood rough sets is proposed, which combines the advantages of both δ-neighborhood and k-NEarest neighbor, and has a better ability to deal with this type of heterogeneous data than the existing models.
107 citations
TL;DR: A hybrid model named intuitionistic fuzzy (IF) rough set is proposed to overcome this limitation and combines the technical advantages of rough set and IF set and can effectively consider the above-mentioned statistical factors.
Abstract: Attribute subset selection is an important issue in data mining and information processing. However, most automatic methodologies consider only the relevance factor between samples while ignoring the diversity factor. This may not allow the utilization value of hidden information to be exploited. For this reason, we propose a hybrid model named intuitionistic fuzzy (IF) rough set to overcome this limitation. The model combines the technical advantages of rough set and IF set and can effectively consider the above-mentioned statistical factors. First, fuzzy information granules based on IF relations are defined and used to characterize the hierarchical structures of the lower and upper approximations of IF rough set within the framework of granular computing. Then, the computation of IF rough approximations and knowledge reduction in IF information systems are investigated. Third, based on the approximations of IF rough set, significance measures are developed to evaluate the approximation quality and classification ability of IF relations. Furthermore, a forward heuristic algorithm for finding one optimal reduct of IF information systems is developed using these measures. Finally, numerical experiments are conducted on public datasets to examine the effectiveness and efficiency of the proposed algorithm in terms of the number of selected attributes, computational time, and classification accuracy.
101 citations
TL;DR: Experimental results show that the RSFSAID algorithm can improve the classification performance of imbalanced data compared to four other algorithms and is beneficial for constructing an effective learning model and reducing the consumption of memory and time.
101 citations
TL;DR: Based on the multi-granularity structure formed by neighborhood rough set, the experimental results over 20 UCI data sets demonstrate that compared with single granularity attribute reduction, the selector can not only generate reducts which may not contribute to poorer classification performances, but also significantly reduce the elapsed time of computing reductS.
95 citations
TL;DR: The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region, and the experimental results prove that the algorithm has strong anti-noise ability.
Abstract: In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability.
TL;DR: An overview of existing information fusion approaches and methods for multi-source, multi-modality, multi -scale, and multi-view information systems from the perspective of objects, attributes, rough approximations, attribute reduction, and decision making is presented.
TL;DR: A pseudo-label neighborhood relation is proposed that can differentiate samples by not only the distance but also the pseudo labels of samples, and both the neighborhood rough set and some corresponding measures can be re-defined.
TL;DR: Through testing nine different ratios of labeled samples in data, the experimental results demonstrate that the approach is superior to previous researches, mainly because: (1) the qualified feature subset derived by the approach can provide better classification performance; (2) the lower time consumption is required in the process of feature selection.
Abstract: Similar to feature selection over completely labeled data, the aim of feature selection over partially labeled data (semi-supervised feature selection) is also to find a feature subset which satisfies the intended constraint. Nevertheless, two difficulties may emerge in the semi-supervised feature selection: (1) labels are incomplete since labeled and unlabeled samples coexist in data; (2) the explanation of the selected feature subset is not clear. Therefore, such two problems will be mainly addressed in our research. Firstly, the unlabeled samples can be predicted through various semi-supervised learning methods. Secondly, the Local Neighborhood Decision Error Rate is proposed to construct multiple fitness functions for evaluating the significance of the candidate feature. Such mechanism not only realizes the ensemble selector in the process of feature selection, but also the qualified feature subset will bring us lower decision errors. Immediately, a heuristic algorithm is re-designed to execute feature selection. Finally, through testing nine different ratios (10%, 20%, … , 90%) of labeled samples in data, the experimental results demonstrate that our approach is superior to previous researches, mainly because: (1) the qualified feature subset derived by our approach can provide better classification performance; (2) the lower time consumption is required in our process of feature selection.
TL;DR: This article proposes and investigates the relationships among the several types of fuzzy soft based fuzzy rough sets, and gives a detailed description of an algorithmic procedure of decision-making for the new approach.
Abstract: In this article, the concepts of fuzzy soft $$\beta$$
-coverings, fuzzy soft $$\beta$$
-neighborhoods and fuzzy soft complement $$\beta$$
-neighborhoods are firstly proposed and some related properties are studied. Then four distinct types of fuzzy soft $$\beta$$
-coverings based fuzzy rough sets are defined. Furthermore, we explore the relationships among the several types of fuzzy soft $$\beta$$
-coverings based fuzzy rough sets. In particular, by means of $$\beta$$
-level subsets of fuzzy soft $$\beta$$
-coverings, some kinds of soft coverings based rough sets are also investigated. Finally, by means of fuzzy soft $$\beta$$
-coverings based fuzzy rough sets, we give a detailed description of an algorithmic procedure of decision-making for the new approach.
TL;DR: A novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information.
Abstract: Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.
TL;DR: This article developed a comprehensive model to tackle decision-making problems, where strong points of view are in the favour; neutral; and against some projects, entities, or plans to manage the vague and uncertainty.
Abstract: In real life, human opinion cannot be limited to yes or no situations as shown in an ordinary fuzzy sets and intuitionistic fuzzy sets but it may be yes, abstain, no, and refusal as treated in Picture fuzzy sets or in Spherical fuzzy (SF) sets. In this article, we developed a comprehensive model to tackle decision-making problems, where strong points of view are in the favour; neutral; and against some projects, entities, or plans. Therefore, a new approach of covering-based spherical fuzzy rough set (CSFRS) models by means of spherical fuzzy β -neighborhoods (SF β -neighborhoods) is adopted to hybrid spherical fuzzy sets with notions of covering the rough set. Then, by using the principle of TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) to present the spherical fuzzy, the TOPSIS approach is presented through CSFRS models by means of SF β -neighborhoods. Via the SF-TOPSIS methodology, a multi-attribute decision-making problem is developed in an SF environment. This model has stronger capabilities than intuitionistic fuzzy sets and picture fuzzy sets to manage the vague and uncertainty. Finally, the proposed method is demonstrated through an example of how the proposed method helps us in decision-making problems.
TL;DR: Experimental studies show that OFS-A3M is superior to traditional feature selection methods with the same numbers of features and state-of-the-art online streaming feature selection algorithms in an online manner.
TL;DR: This paper investigates uncertainty measurement for a fuzzy relation information system and the axiom definition of the granularity measurement of the uncertainty for fuzzy relation Information systems is proposed by means of its information structures.
Abstract: A fuzzy relation information system may be viewed as an information system with fuzzy relations. Uncertainty measurement is a critical evaluating tool. This paper investigates uncertainty measurement for a fuzzy relation information system. The concept of information structures in a fuzzy relation information system is first described by using set vectors. Then, dependence between information structures in a fuzzy relation information system is given. Next, the axiom definition of the granularity measurement of the uncertainty for fuzzy relation information systems is proposed by means of its information structures. Based upon this axiom definition, information granulation and rough entropy in a fuzzy relation information system are proposed. Moreover, information entropy, information amount, joint entropy, and condition entropy in a fuzzy relation information system are also considered. To show the feasibility of the proposed measures for uncertainty of a fuzzy relation information system, effectiveness analysis is conducted from the angle of statistics. Finally, characterizations of fuzzy relation information systems under a compatible homomorphism are obtained. These results will be helpful for understanding the essence of uncertainty in a fuzzy relation information system.
01 Oct 2019
TL;DR: This paper presents respective new approaches to decision-making problems based on the IFNSS and IFNSRS models in an algorithmic format and presents illustrative examples in order to show that these decision- making methods can be fruitfully employed to solve real life problems.
Abstract: In this paper, we introduce three novel hybrid models, namely, intuitionistic fuzzy N-soft sets (IFNSSs), N-soft rough intuitionistic fuzzy sets and intuitionistic fuzzy N-soft rough sets (IFNSRSs). We discuss some of their respective properties. We also present the lower and upper intuitionistic fuzzy N-soft rough approximation operators and investigate their properties. Furthermore, we present respective new approaches to decision-making problems based on the IFNSS and IFNSRS models in an algorithmic format. Finally, we present illustrative examples in order to show that these decision-making methods can be fruitfully employed to solve real life problems.
TL;DR: An adaptive IDS based on Fuzzy Rough sets for attribute selection and Allen's interval algebra is applied on network trace datasets in order to select a huge number of attack data for effective prediction of attacks in WSNs and a fuzzy and rough set based nearest neighborhood algorithm (FRNN) is proposed in this article for effective classification of network trace dataset.
TL;DR: This work combines rough set theory and the IF decision-making approach in order to design a novel procedure for making decisions that is more effective to deal with MAGDM problems with IF information than the existing MAGDM methods.
TL;DR: This paper investigates a novel classification algorithm, called sequential three-way classifier with justifiable subspace, inspired by the principle of justifiable granularity, which generally exhibits a better classification performance involving fewer attributes.
Abstract: Sequential three-way decisions approach has been demonstrated as an effective methodology of human problem solving with the aid of multiple levels of granularity. Searching an appropriate information granularity for decision or classification is a crucial issue. In this paper, inspired by the principle of justifiable granularity, we investigate a novel classification algorithm, called sequential three-way classifier with justifiable subspace. The major contribution of this study is threefold. First, in training model, with an investigation of the essence of information granularity in rough sets theory, the justifiable attribute subspace is located in an interval with local and global notions. Second, in light of the advantages of attribute reduction technology, the local and global attribute subspaces are determined by core and reduct, respectively. Third, a novel dynamic tri-partition-based predicting strategy is presented with the aid of neighborhood monotonic property. Finally, several experiments are undertaken to verify the effectiveness of the proposed method. Compared with several state-of-the-art classifiers, the proposed algorithm generally exhibits a better classification performance involving fewer attributes.
TL;DR: In this paper, the structural information of rough set approximations is considered, that is, the composition of an approximation in terms of equivalence classes is useful to retain structural information.
Abstract: A major application of rough set theory is concept analysis for deciding if an object is an instance of a concept based on its description. Objects with the same description form an equivalence class and the family of equivalence classes is used to define rough set approximations. When deriving the decision rules from approximations, the description of an equivalence class is the left-hand-side of a decision rule. Therefore, it is useful to retain structural information of approximations, that is, the composition of an approximation in terms of equivalence classes. However, existing studies do not explicitly consider the structural information. To address this issue, we introduce structured rough set approximations in both complete and incomplete information tables, which serve as a basis for three-way decisions with rough sets. In a complete table, we define a family of conjunctively definable concepts. The structured three-way approximations are three structured positive, boundary and negative regions given by three sets of conjunctively definable concepts. By adopting a possible-world semantics, we introduce the notion of conjunctively definable interval concepts in an incomplete table, which is used to construct the structured three-way approximations. The internal structure of structured approximations contributes to sound semantics of rough set approximations and is directly and explicitly related to three-way decision rules.
TL;DR: A three-way decision-theoretic rough set model is defined based on three types of criteria for defining attribute reduct including the positive region, decision cost and mutual information and combined to a multi-objective optimization problem.
TL;DR: This research study introduces intuitionistic fuzzy rough graphs, and describes certain types of intuitionistic furry rough graphs with several examples, and develops efficient algorithms to solve decision-making problems and compute time complexity of each algorithm.
Abstract: Intuitionistic fuzzy sets and rough sets are two different mathematical models to deal the problem of how to understand and manipulate imperfect knowledge. An intuitionistic fuzzy rough framework is made by combining these two models, which is a more flexible and expressive for modeling and processing incomplete information in information systems. In this research study, we introduce intuitionistic fuzzy rough graphs, and describe certain types of intuitionistic fuzzy rough graphs with several examples. We present applications of intuitionistic fuzzy rough graphs in decision-making problems. We develop efficient algorithms to solve decision-making problems and compute time complexity of each algorithm.
TL;DR: A multiclass three-way decision-theoretic rough set model that can be directly applied to traditional cost-sensitive learning problems and can achieve higher classification accuracy and lower misclassification cost is presented.
TL;DR: Five new different types of soft coverings based rough sets are built by means of soft neighborhoods, soft complementary neighborhoods and soft adhesions and two special algorithms are given to solve an actual problem.
Abstract: Hybrid soft set model is an important topic for dealing with uncertainty. By means of soft neighborhoods, soft complementary neighborhoods and soft adhesions, we build five new different types of soft coverings based rough sets and study related properties. The relationships between soft rough sets and soft covering based rough sets are investigated. Finally, we give two special algorithms based on the first two types of soft coverings based rough sets and apply the two special algorithms to solve an actual problem.
TL;DR: This paper proposes new algorithms for decision-making based on the probabilistic soft set theory, and the notion of dual probabilism soft sets is proposed, and also, its application in decision- making is investigated.
Abstract: Since its introduction by Molodstov (Computers & Mathematics with Applications 37(4):19–31 1999), soft set theory has been widely applied in various fields of study. Soft set theory has also been combined with other theories like fuzzy sets theory, rough sets theory, and probability theory. The combination of soft sets and probability theory generates probabilistic soft set theory. However, decision-making based on the probabilistic soft set theory has not been discussed in the literature. In this paper, we propose new algorithms for decision-making based on the probabilistic soft set theory. An example to show the application of these algorithms is given, and its possible extensions and reinterpretations are discussed. Inspired by realistic situations, the notion of dual probabilistic soft sets is proposed, and also, its application in decision-making is investigated.
TL;DR: New measures of uncertainty for an interval-valued information system are investigated by using the information structures and the rough entropy of a rough set is proposed by means of information granulation.