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Bearing fault diagnosis algorithm based on granular computing. GRANULAR COMPUTING 2022. [DOI: 10.1007/s41066-022-00328-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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2
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Li B, Gong X, Wang C, Wu R, Bian T, Li Y, Wang Z, Luo R. MMD-encouraging convolutional autoencoder: a novel classification algorithm for imbalanced data. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02235-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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3
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Khan A, Abosuliman SS, Abdullah S, Ayaz M. A Decision Support Model for Hotel Recommendation Based on the Online Consumer Reviews Using Logarithmic Spherical Hesitant Fuzzy Information. ENTROPY (BASEL, SWITZERLAND) 2021; 23:432. [PMID: 33917646 PMCID: PMC8067595 DOI: 10.3390/e23040432] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 03/22/2021] [Accepted: 03/23/2021] [Indexed: 11/17/2022]
Abstract
Spherical hesitant fuzzy sets have recently become more popular in various fields. It was proposed as a generalization of picture hesitant fuzzy sets and Pythagorean hesitant fuzzy sets in order to deal with uncertainty and fuzziness information. Technique of Aggregation is one of the beneficial tools to aggregate the information. It has many crucial application areas such as decision-making, data mining, medical diagnosis, and pattern recognition. Keeping in view the importance of logarithmic function and aggregation operators, we proposed a novel algorithm to tackle the multi-attribute decision-making (MADM) problems. First, novel logarithmic operational laws are developed based on the logarithmic, t-norm, and t-conorm functions. Using these operational laws, we developed a list of logarithmic spherical hesitant fuzzy weighted averaging/geometric aggregation operators to aggregate the spherical hesitant fuzzy information. Furthermore, we developed the spherical hesitant fuzzy entropy to determine the unknown attribute weight information. Finally, the design principles for the spherical hesitant fuzzy decision-making have been developed, and a practical case study of hotel recommendation based on the online consumer reviews has been taken to illustrate the validity and superiority of presented approach. Besides this, a validity test is conducted to reveal the advantages and effectiveness of developed approach. Results indicate that the proposed method is suitable and effective for the decision process to evaluate their best alternative.
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Affiliation(s)
- Aziz Khan
- Department of Mathematics, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan; (A.K.); (M.A.)
| | - Shougi S. Abosuliman
- Department of Transportation and Port Management, Faculty of Maritime Studies, King Abdulaziz University, Jeddah 21588, Saudi Arabia;
| | - Saleem Abdullah
- Department of Mathematics, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan; (A.K.); (M.A.)
| | - Muhammad Ayaz
- Department of Mathematics, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan; (A.K.); (M.A.)
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4
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Liu YH, Fan HY. Classifying univariate uncertain data. APPL INTELL 2021. [DOI: 10.1007/s10489-020-01911-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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5
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Mao Y, He Z. Dual-Y network: infrared-visible image patches matching via semi-supervised transfer learning. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01996-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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6
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Wang X, Ding W, Liu H, Huang X. Shape recognition through multi-level fusion of features and classifiers. GRANULAR COMPUTING 2020. [DOI: 10.1007/s41066-019-00164-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Abstract
AbstractThe use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random subspace methods to create different samples and features for improved classification performance of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs.
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Cheng G, Chen Q, Zhang R. Prediction of phosphorylation sites based on granular support vector machine. GRANULAR COMPUTING 2019. [DOI: 10.1007/s41066-019-00202-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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10
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Heidari M, Moattar MH. Discriminative geodesic Gaussian process latent variable model for structure preserving dimension reduction in clustering and classification problems. Neural Comput Appl 2019. [DOI: 10.1007/s00521-017-3273-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Kumar A, Halder A. Active Learning Using Fuzzy-Rough Nearest Neighbor Classifier for Cancer Prediction from Microarray Gene Expression Data. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001420570013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Cancer prediction from gene expression data is a very challenging area of research in the field of computational biology and bioinformatics. Conventional classifiers are often unable to achieve desired accuracy due to the lack of ‘sufficient’ training patterns in terms of clinically labeled samples. Active learning technique, in this respect, can be useful as it automatically finds only few most informative (or confusing) samples to get their class labels from the experts and those are added to the training set, which can improve the accuracy of the prediction consequently. A novel active learning technique using fuzzy-rough nearest neighbor classifier (ALFRNN) is proposed in this paper for cancer classification from microarray gene expression data. The proposed ALFRNN method is capable of dealing with the uncertainty, overlapping and indiscernibility often present in cancer subtypes (classes) of the gene expression data. The performance of the proposed method is tested using different real-life microarray gene expression cancer datasets and its performance is compared with five other state-of-the-art techniques (out of which three are active learning-based and two are traditional classification methods) in terms of percentage accuracy, precision, recall, [Formula: see text]-measures and kappa. Superiority of the proposed method over the other counterpart algorithms is established from experimental results for cancer prediction and results of the paired [Formula: see text]-test confirm statistical significance of the results in favor of the proposed method for almost all the datasets.
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Affiliation(s)
- Ansuman Kumar
- Department of Computer Application, North-Eastern Hill University, Tura Campus, Meghalaya 794002, India
| | - Anindya Halder
- Department of Computer Application, North-Eastern Hill University, Tura Campus, Meghalaya 794002, India
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12
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Li Q, Chen L, Li X, Xia S, Kang Y. An improved random forests approach for interactive lobar segmentation on emphysema detection. GRANULAR COMPUTING 2019. [DOI: 10.1007/s41066-019-00171-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Motevali MM, Shanghooshabad AM, Aram RZ, Keshavarz H. WHO: A New Evolutionary Algorithm Bio-Inspired by Wildebeests with a Case Study on Bank Customer Segmentation. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001419590171] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Numerous evolutionary algorithms have been proposed which are inspired by the amazing lives of creatures, such as animals, insects, and birds. Each inspired algorithm has its own advantages and disadvantages, and has its own way to accomplish exploration and exploitation. In this paper, a new evolutionary algorithm with novel concepts, called Wildebeests Herd Optimization (WHO), is proposed. This algorithm is inspired by the splendid life of wildebeests in Africa. Moving and migration are inseparable from wildebeests’ lives. When a wildebeest wants to choose its path during migration, it considers the best path known to itself, the location of the more mature wildebeests in the crowd, and the direction of wildebeests with high mobility. The WHO algorithm imitates these traits, and can concurrently explore and exploit the search space. For validating WHO, it is applied to optimization problems and data mining tasks. It is demonstrated that WHO outperforms other evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization, in the assessed problems. Then, WHO is applied to the customer segmentation problem. Customer segmentation is one of the most important tasks of data mining, especially in the banking sector. In this paper, the customers of a bank with current accounts are segmented using WHO based on four aspects: profitability, cost, loyalty and credit; some of these aspects are calculated in a novel way. The results were welcome by the bank authorities.
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Affiliation(s)
- Mohammad Mahdi Motevali
- Computer Engineering Department, Islamic Azad University, Semnan Branch, Semnan 3519744571, Iran
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran Province, Tehran, Al Ahmad Street No. 7. Jalal, Iran
| | - Ali Mohammadi Shanghooshabad
- Computer Engineering Department, Islamic Azad University, Semnan Branch, Semnan 3519744571, Iran
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran Province, Tehran, Al Ahmad Street No. 7. Jalal, Iran
| | - Reza Zohouri Aram
- Computer Engineering Department, Islamic Azad University, Semnan Branch, Semnan 3519744571, Iran
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran Province, Tehran, Al Ahmad Street No. 7. Jalal, Iran
| | - Hamidreza Keshavarz
- Computer Engineering Department, Islamic Azad University, Semnan Branch, Semnan 3519744571, Iran
- Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran Province, Tehran, Al Ahmad Street No. 7. Jalal, Iran
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Matrix Method for the Optimal Scale Selection of Multi-Scale Information Decision Systems. MATHEMATICS 2019. [DOI: 10.3390/math7030290] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In multi-scale information systems, the information is often characterized at multi scales and multi levels. To facilitate the computational process of multi-scale information systems, we employ the matrix method to represent the multi-scale information systems and to select the optimal scale combination of multi-scale decision information systems in this study. To this end, we first describe some important concepts and properties of information systems using some relational matrices. The relational matrix is then introduced into multi-scale information systems, and used to describe some main concepts in systems, including the lower and upper approximate sets and the consistence of systems. Furthermore, from the view of the relation matrix, the scale significance is defined to describe the global optimal scale and the local optimal scale of multi-scale information systems. Finally, the relational matrix is used to compute the scale significance and to construct the optimal scale selection algorithms. The efficiency of these algorithms is examined by several practical examples and experiments.
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Dai G, Hu Y, Yang Y, Zhang N, Abraham A, Liu H. A novel fuzzy rule extraction approach using Gaussian kernel-based granular computing. Knowl Inf Syst 2019. [DOI: 10.1007/s10115-018-1318-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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16
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Wang D, Song W, Pedrycz W. A two stage forecasting approach for interval-valued time series. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-18173] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Degang Wang
- School of Control Science and Engineering, Dalian University of Technology, Dalian, China
| | - Wenyan Song
- School of Economics, Dongbei University of Finance and Economics, Dalian, China
| | - Witold Pedrycz
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4, Canada
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
- Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
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17
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Hesitant probabilistic fuzzy set based time series forecasting method. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0126-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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18
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Nature-inspired framework of ensemble learning for collaborative classification in granular computing context. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0122-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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19
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Amezcua J, Melin P. A new fuzzy learning vector quantization method for classification problems based on a granular approach. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0120-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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20
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Liu Y, Yang M, Zhai J, Bai M. Portfolio selection of the defined contribution pension fund with uncertain return and salary: A multi-period mean-variance model. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-171440] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yali Liu
- School of Economics and Management, Beihang University, Beijing, China
| | - Meiying Yang
- School of Economics and Management, Beihang University, Beijing, China
| | - Jia Zhai
- School of Economics and Management, Beihang University, Beijing, China
| | - Manying Bai
- School of Economics and Management, Beihang University, Beijing, China
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21
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Wang JQ, Zhang X, Zhang HY. Hotel recommendation approach based on the online consumer reviews using interval neutrosophic linguistic numbers. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-171421] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jian-Qiang Wang
- School of Business, Central South University, Changsha, China
| | - Xu Zhang
- School of Business, Central South University, Changsha, China
| | - Hong-Yu Zhang
- School of Business, Central South University, Changsha, China
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22
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Gong Y, Yang S, Ma H, Ge M. Fuzzy regression model based on geometric coordinate points distance and application to performance evaluation. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-171433] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yanbing Gong
- School of Business Administration, Hohai University, Changzhou, China
| | - Shuxin Yang
- School of Business Administration, Hohai University, Changzhou, China
| | - Hailiang Ma
- School of Business Administration, Hohai University, Changzhou, China
| | - Min Ge
- School of Business Administration, Hohai University, Changzhou, China
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Liu H, Zhang L. Fuzzy rule-based systems for recognition-intensive classification in granular computing context. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0076-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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24
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Multi-task learning for intelligent data processing in granular computing context. GRANULAR COMPUTING 2017. [DOI: 10.1007/s41066-017-0065-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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25
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Liu N, Meng S. Approaches to the selection of cold chain logistics enterprises under hesitant fuzzy environment based on decision distance measures. GRANULAR COMPUTING 2017. [DOI: 10.1007/s41066-017-0051-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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26
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Semi-random partitioning of data into training and test sets in granular computing context. GRANULAR COMPUTING 2017. [DOI: 10.1007/s41066-017-0049-2] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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27
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GIFIHIA operator and its application to the selection of cold chain logistics enterprises. GRANULAR COMPUTING 2017. [DOI: 10.1007/s41066-017-0038-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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28
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Fuzzy information granulation towards interpretable sentiment analysis. GRANULAR COMPUTING 2017. [DOI: 10.1007/s41066-017-0043-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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