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Tavares E, Silva AM, Moita GF. A fast clustering algorithm for evolving fuzzy classifier based on samples mean. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Evolving models have shown great success in processing non-stationary data that change their characteristics over time. Motivated by elaborating a high-performance model for data classification, the present work proposes a new evolving fuzzy classifier. The proposed model, named evolving Fuzzy Mean Classifier (eFMC), has a low computational cost and is autonomous, i.e., no has user-defined parameters. The eFMC is based on fuzzy clustering structures, where the membership degree between the samples and the clusters is used to obtain the output. In the proposed approach, each class is represented by a cluster, and new clusters are created whenever a new class is discovered. The centers of the clusters are updated through the sample’s means calculated incrementally. Computational experiments were carried out to evaluate and compare the performance of the eFMC in terms of accuracy and processing time. Experimental results and comparisons against alternative state-of-the-art evolving classifiers show that the eFMC is accurate and fast, characteristics essential for adaptive classifiers, especially in online and real-time environments.
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Affiliation(s)
- Emmanuel Tavares
- Federal Center for Technological Education of MinasGerais - CEFET-MG, Belo Horizonte, Brazil
| | - Alisson Marques Silva
- Federal Center for Technological Education of MinasGerais - CEFET-MG, Belo Horizonte, Brazil
| | - Gray Farias Moita
- Federal Center for Technological Education of MinasGerais - CEFET-MG, Belo Horizonte, Brazil
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2
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Fuzzy clustering algorithms with distance metric learning and entropy regularization. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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3
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ASCRClu: an adaptive subspace combination and reduction algorithm for clustering of high-dimensional data. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-020-00884-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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4
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Jin L, Zhao S, Zhang C, Gao W, Dou Y, Lu M. Adaptive soft subspace clustering combining within-cluster and between-cluster information. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-190146] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Liying Jin
- School of Mechanical Engineering, Xi’an Jiaotong University, Shannxi Xi’an, China
| | - Shengdun Zhao
- School of Mechanical Engineering, Xi’an Jiaotong University, Shannxi Xi’an, China
| | - Congcong Zhang
- School of Mechanical Engineering, Xi’an Jiaotong University, Shannxi Xi’an, China
| | - Wei Gao
- School of Mechanical Engineering, Xi’an Jiaotong University, Shannxi Xi’an, China
| | - Yao Dou
- School of Mechanical Engineering, Xi’an Jiaotong University, Shannxi Xi’an, China
| | - Mengkang Lu
- School of Mechanical Engineering, Xi’an Jiaotong University, Shannxi Xi’an, China
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Meng L, Tan AH, Miao C. Salience-aware adaptive resonance theory for large-scale sparse data clustering. Neural Netw 2019; 120:143-157. [DOI: 10.1016/j.neunet.2019.09.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 09/08/2019] [Accepted: 09/10/2019] [Indexed: 11/17/2022]
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Jin L, Zhi X, Zhao S. Enhanced subspace clustering through combining Minkowski distance and Cosine dissimilarity. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-18563] [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)
- Liying Jin
- School of Mechanical Engineering, Xi’an Jiaotong University, Shannxi Xi’an, China
| | - Xiaobin Zhi
- School of Science, Xi’an University of Post and Telecommunications, Shannxi Xi’an, China
| | - Shengdun Zhao
- School of Mechanical Engineering, Xi’an Jiaotong University, Shannxi Xi’an, China
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Wang L, Hao Z, Cai R, Wen W. Enhanced soft subspace clustering through hybrid dissimilarity. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015. [DOI: 10.3233/ifs-141517] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Lijuan Wang
- Faculty of Computer, Guangdong University of Technology, Guangzhou, P.R. China
- State Key Laboratory for Novel Software Technology, Nanjing University, P.R. China
| | - Zhifeng Hao
- Faculty of Computer, Guangdong University of Technology, Guangzhou, P.R. China
| | - Ruichu Cai
- Faculty of Computer, Guangdong University of Technology, Guangzhou, P.R. China
- State Key Laboratory for Novel Software Technology, Nanjing University, P.R. China
| | - Wen Wen
- Faculty of Computer, Guangdong University of Technology, Guangzhou, P.R. China
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Tang X, Zeng W, Wang N, Yang J. An adaptive RV measure based fuzzy weighting subspace clustering (ARV-FWSC) for fMRI data analysis. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2015.07.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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9
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A novel soft subspace clustering algorithm with noise detection for high dimensional datasets. Soft comput 2015. [DOI: 10.1007/s00500-015-1756-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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10
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A modified fuzzy min–max neural network for data clustering and its application to power quality monitoring. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2014.09.050] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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