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Jun Ye - One of the best experts on this subject based on the ideXlab platform.
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similarity measures of intuitionistic fuzzy sets based on Cosine function for the decision making of mechanical design schemes
Journal of Intelligent and Fuzzy Systems, 2015Co-Authors: Jun YeAbstract:Based on Cosine function and the information carried by the membership degrees, nonmembership degree and hesitancy degree in intuitionistic fuzzy sets (IFSs), this paper proposes two new Cosine similarity measures and weighted Cosine similarity measures between IFSs. Then, we give the comparative analysis of various trigonometric similarity measures by several numerical examples to illustrate the effectiveness of the developed Cosine similarity measures of IFSs. Furthermore, we develop a decision- making method using the weighted Cosine similarity measures for choosing mechanical design schemes (alternatives). Finally, a decision-making example on choosing mechanical design schemes is given to demonstrate the applications and efficiency of the proposed decision-making method.
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improved Cosine similarity measures of simplified neutrosophic sets for medical diagnoses
2015Co-Authors: Jun YeAbstract:Simplified neutrosophic set Single valued neutrosophic set Interval neutrosophic set Cosine similarity measure Medical diagnosis a b s t r a c t Objective: In pattern recognition and medical diagnosis, similarity measure is an important mathematical tool. To overcome some disadvantages of existing Cosine similarity measures of simplified neutrosophic sets (SNSs) in vector space, this paper proposed improved Cosine similarity measures of SNSs based on Cosine function, including single valued neutrosophic Cosine similarity measures and interval neutrosophic Cosine similarity measures. Then, weighted Cosine similarity measures of SNSs were introduced by taking into account the importance of each element. Further, a medical diagnosis method using the improved Cosine similarity measures was proposed to solve medical diagnosis problems with simplified neutrosophic information. Materials and methods: The improved Cosine similarity measures between SNSs were introduced based on Cosine function. Then, we compared the improved Cosine similarity measures of SNSs with existing Cosine similarity measures of SNSs by numerical examples to demonstrate their effectiveness and rationality for overcoming some shortcomings of existing Cosine similarity measures of SNSs in some cases. In the medical diagnosis method, we can find a proper diagnosis by the Cosine similarity measures between the symptoms and considered diseases which are represented by SNSs. Then, the medical diagnosis method based on the improved Cosine similarity measures was applied to two medical diagnosis problems to show the applications and effectiveness of the proposed method. Results: Two numerical examples all demonstrated that the improved Cosine similarity measures of SNSs based on the Cosine function can overcome the shortcomings of the existing Cosine similarity measures between two vectors in some cases. By two medical diagnoses problems, the medical diagnoses using various similarity measures of SNSs indicated the identical diagnosis results and demonstrated the effectiveness and rationality of the diagnosis method proposed in this paper. Conclusions: The improved Cosine measures of SNSs based on Cosine function can overcome some drawbacks of existing Cosine similarity measures of SNSs in vector space, and then their diagnosis method is very suitable for handling the medical diagnosis problems with simplified neutrosophic information and demonstrates the effectiveness and rationality of medical diagnoses.
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improved Cosine similarity measures of simplified neutrosophic sets for medical diagnoses
Artificial Intelligence in Medicine, 2015Co-Authors: Jun YeAbstract:We proposed improved Cosine similarity measures of simplified neutrosophic sets (SNSs) based on Cosine function, including single valued neutrosophic Cosine similarity measures and interval neutrosophic Cosine similarity measures, to overcome some disadvantages of existing Cosine similarity measures of SNSs.We presented a medical diagnosis method based on the improved Cosine similarity measures to solve medical diagnosis problems with simplified neutrosophic information.Two medical diagnosis problems were given to show the effectiveness and rationality of the diagnosis method using the improved Cosine similarity measures. ObjectiveIn pattern recognition and medical diagnosis, similarity measure is an important mathematical tool. To overcome some disadvantages of existing Cosine similarity measures of simplified neutrosophic sets (SNSs) in vector space, this paper proposed improved Cosine similarity measures of SNSs based on Cosine function, including single valued neutrosophic Cosine similarity measures and interval neutrosophic Cosine similarity measures. Then, weighted Cosine similarity measures of SNSs were introduced by taking into account the importance of each element. Further, a medical diagnosis method using the improved Cosine similarity measures was proposed to solve medical diagnosis problems with simplified neutrosophic information. Materials and methodsThe improved Cosine similarity measures between SNSs were introduced based on Cosine function. Then, we compared the improved Cosine similarity measures of SNSs with existing Cosine similarity measures of SNSs by numerical examples to demonstrate their effectiveness and rationality for overcoming some shortcomings of existing Cosine similarity measures of SNSs in some cases. In the medical diagnosis method, we can find a proper diagnosis by the Cosine similarity measures between the symptoms and considered diseases which are represented by SNSs. Then, the medical diagnosis method based on the improved Cosine similarity measures was applied to two medical diagnosis problems to show the applications and effectiveness of the proposed method. ResultsTwo numerical examples all demonstrated that the improved Cosine similarity measures of SNSs based on the Cosine function can overcome the shortcomings of the existing Cosine similarity measures between two vectors in some cases. By two medical diagnoses problems, the medical diagnoses using various similarity measures of SNSs indicated the identical diagnosis results and demonstrated the effectiveness and rationality of the diagnosis method proposed in this paper. ConclusionsThe improved Cosine measures of SNSs based on Cosine function can overcome some drawbacks of existing Cosine similarity measures of SNSs in vector space, and then their diagnosis method is very suitable for handling the medical diagnosis problems with simplified neutrosophic information and demonstrates the effectiveness and rationality of medical diagnoses.
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Intuitionistic Fuzzy and Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measures for Pattern Recognitions
International Journal of Advancements in Computing Technology, 2013Co-Authors: Jun YeAbstract:Abstract In this work, considering the information carried by the membership degree and the nonmembership degree in intuitionistic fuzzy sets (IFSs) and interval-valued intuitionistic fuzzy sets (IVIFSs) as vector representations, in the vector space we propose novel Cosine similarity measures for IFSs based on the Cosine similarity measure (angular coefficient) between Fuzzy Sets and Cosine similarity measures between IVIFSs based on the extension of Cosine similarity measures between IFSs. Then, applied examples for pattern recognitions are presented to demonstrate the efficiency of the proposed Cosine similarity measures.
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Cosine similarity measures for intuitionistic fuzzy sets and their applications
Mathematical and Computer Modelling, 2011Co-Authors: Jun YeAbstract:In this work, considering the information carried by the membership degree and the non-membership degree in Atanassov's intuitionistic fuzzy sets (IFSs) as a vector representation with the two elements, a Cosine similarity measure and a weighted Cosine similarity measure between IFSs are proposed based on the concept of the Cosine similarity measure for fuzzy sets. To demonstrate the efficiency of the proposed Cosine similarity measures, the existing similarity measures between IFSs are compared with the Cosine similarity measure between IFSs by numerical examples. Finally, the Cosine similarity measures are applied to pattern recognition and medical diagnosis.
Xiaohong Chen - One of the best experts on this subject based on the ideXlab platform.
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Cosine Similarity Measure between Hybrid Intuitionistic Fuzzy Sets and Its Application in Medical Diagnosis
Computational and Mathematical Methods in Medicine, 2018Co-Authors: Xiaohong Chen, Dan PengAbstract:In this paper, a Cosine similarity measure between hybrid intuitionistic fuzzy sets is proposed. The aim of the paper is to investigate the Cosine similarity measure with hybrid intuitionistic fuzzy information and apply it to medical diagnosis. Firstly, we construct the Cosine similarity measure between hybrid intuitionistic fuzzy sets, and the relevant properties are also discussed. In order to obtain a reasonable evaluation in group decision, the weight of experts under different attributes is determined by the projection of individual decision information on the ideal decision information, where the ideal decision information is the average values of each expert’s evaluation. Furthermore, we propose a decision method for medical diagnosis based on the Cosine similarity measure between hybrid intuitionistic fuzzy sets, and the patient can be diagnosed with the disease according to the values of proposed Cosine similarity measure. Finally, an example is given to illustrate feasibility and effectiveness of the proposed Cosine similarity measure, which is also compared with the existing similarity measures.
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the intuitionistic fuzzy linguistic Cosine similarity measure and its application in pattern recognition
Complexity, 2018Co-Authors: Xiaohong Chen, Dan PengAbstract:We propose the Cosine similarity measures for intuitionistic fuzzy linguistic sets (IFLSs) and interval-valued intuitionistic fuzzy linguistic sets (IVIFLSs), which are expressed by the linguistic scale function based on the Cosine function. Then, the weighted Cosine similarity measure and the ordered weighted Cosine similarity measure for IFLSs and IVIFLSs are introduced by taking into account the importance of each element, and the properties of the Cosine similarity measures are also given. The main advantage of the proposed Cosine similarity measures is that the decision-makers can flexibly select the linguistic scale function depending on the actual semantic situation. Finally, we present the application of the Cosine similarity measures for intuitionistic fuzzy linguistic term sets and interval-valued intuitionistic fuzzy linguistic term sets to pattern recognition and medical diagnosis, and the existing Cosine similarity measures are compared with the proposed Cosine similarity measures by the illustrative example.
Baoli Li - One of the best experts on this subject based on the ideXlab platform.
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distance weighted Cosine similarity measure for text classification
Intelligent Data Engineering and Automated Learning, 2013Co-Authors: Baoli LiAbstract:In Vector Space Model, Cosine is widely used to measure the similarity between two vectors. Its calculation is very efficient, especially for sparse vectors, as only the non-zero dimensions need to be considered. As a fundamental component, Cosine similarity has been applied in solving different text mining problems, such as text classification, text summarization, information retrieval, question answering, and so on. Although it is popular, the Cosine similarity does have some problems. Starting with a few synthetic samples, we demonstrate some problems of Cosine similarity: it is overly biased by features of higher values and does not care much about how many features two vectors share. A distance weighted Cosine similarity metric is thus proposed. Extensive experiments on text classification exhibit the effectiveness of the proposed metric.
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IDEAL - Distance Weighted Cosine Similarity Measure for Text Classification
Intelligent Data Engineering and Automated Learning – IDEAL 2013, 2013Co-Authors: Baoli LiAbstract:In Vector Space Model, Cosine is widely used to measure the similarity between two vectors. Its calculation is very efficient, especially for sparse vectors, as only the non-zero dimensions need to be considered. As a fundamental component, Cosine similarity has been applied in solving different text mining problems, such as text classification, text summarization, information retrieval, question answering, and so on. Although it is popular, the Cosine similarity does have some problems. Starting with a few synthetic samples, we demonstrate some problems of Cosine similarity: it is overly biased by features of higher values and does not care much about how many features two vectors share. A distance weighted Cosine similarity metric is thus proposed. Extensive experiments on text classification exhibit the effectiveness of the proposed metric.
Zhansheng Duan - One of the best experts on this subject based on the ideXlab platform.
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recursive lmmse filtering for target tracking with range and direction Cosine measurements
International Conference on Information Fusion, 2010Co-Authors: Zhansheng Duan, Rong X. LiAbstract:Due to the nonlinear relationship between Cartesian coordinates and range-direction-Cosine coordinates, target tracking of state described in Cartesian coordinates with range and direction Cosine measurements is a nonlinear filtering problem. Measurement conversion based Kalman filter available for this type of problem has some serious drawbacks. Depending on whether measurement of the third direction Cosine is directly available, two recursive linear minimum mean-squared error (LMMSE) filters for target tracking with range and direction Cosine measurements are developed in this paper. Illustrative numerical examples show that in terms of credibility and accuracy, the proposed filters should be preferred.
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FUSION - Recursive LMMSE filtering for target tracking with range and direction Cosine measurements
2010 13th International Conference on Information Fusion, 2010Co-Authors: Zhansheng Duan, X. Rong LiAbstract:Due to the nonlinear relationship between Cartesian coordinates and range-direction-Cosine coordinates, target tracking of state described in Cartesian coordinates with range and direction Cosine measurements is a nonlinear filtering problem. Measurement conversion based Kalman filter available for this type of problem has some serious drawbacks. Depending on whether measurement of the third direction Cosine is directly available, two recursive linear minimum mean-squared error (LMMSE) filters for target tracking with range and direction Cosine measurements are developed in this paper. Illustrative numerical examples show that in terms of credibility and accuracy, the proposed filters should be preferred.
Dan Peng - One of the best experts on this subject based on the ideXlab platform.
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Cosine Similarity Measure between Hybrid Intuitionistic Fuzzy Sets and Its Application in Medical Diagnosis
Computational and Mathematical Methods in Medicine, 2018Co-Authors: Xiaohong Chen, Dan PengAbstract:In this paper, a Cosine similarity measure between hybrid intuitionistic fuzzy sets is proposed. The aim of the paper is to investigate the Cosine similarity measure with hybrid intuitionistic fuzzy information and apply it to medical diagnosis. Firstly, we construct the Cosine similarity measure between hybrid intuitionistic fuzzy sets, and the relevant properties are also discussed. In order to obtain a reasonable evaluation in group decision, the weight of experts under different attributes is determined by the projection of individual decision information on the ideal decision information, where the ideal decision information is the average values of each expert’s evaluation. Furthermore, we propose a decision method for medical diagnosis based on the Cosine similarity measure between hybrid intuitionistic fuzzy sets, and the patient can be diagnosed with the disease according to the values of proposed Cosine similarity measure. Finally, an example is given to illustrate feasibility and effectiveness of the proposed Cosine similarity measure, which is also compared with the existing similarity measures.
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the intuitionistic fuzzy linguistic Cosine similarity measure and its application in pattern recognition
Complexity, 2018Co-Authors: Xiaohong Chen, Dan PengAbstract:We propose the Cosine similarity measures for intuitionistic fuzzy linguistic sets (IFLSs) and interval-valued intuitionistic fuzzy linguistic sets (IVIFLSs), which are expressed by the linguistic scale function based on the Cosine function. Then, the weighted Cosine similarity measure and the ordered weighted Cosine similarity measure for IFLSs and IVIFLSs are introduced by taking into account the importance of each element, and the properties of the Cosine similarity measures are also given. The main advantage of the proposed Cosine similarity measures is that the decision-makers can flexibly select the linguistic scale function depending on the actual semantic situation. Finally, we present the application of the Cosine similarity measures for intuitionistic fuzzy linguistic term sets and interval-valued intuitionistic fuzzy linguistic term sets to pattern recognition and medical diagnosis, and the existing Cosine similarity measures are compared with the proposed Cosine similarity measures by the illustrative example.
