메뉴 건너뛰기




Volumn 1, Issue 1, 2010, Pages 3-15

A semi-supervised dynamic version of Fuzzy K-Nearest Neighbours to monitor evolving systems

Author keywords

Classification; Dynamic classes; Evolving systems; Fault diagnosis; Semi supervised method

Indexed keywords

ADAPTIVE CLASSIFICATION; CLASSIFICATION; CLASSIFICATION METHODS; DETECTION PHASE; EVOLVING SYSTEMS; FAULT DIAGNOSIS; K-NEAREST NEIGHBOURS; REAL-WORLD APPLICATION; SECOND PHASE; SEMI-SUPERVISED; SEMI-SUPERVISED METHOD; VALIDATION PHASE;

EID: 79952317715     PISSN: 18686478     EISSN: 18686486     Source Type: Journal    
DOI: 10.1007/s12530-010-9001-2     Document Type: Review
Times cited : (27)

References (37)
  • 2
    • 1642312436 scopus 로고    scopus 로고
    • A fuzzy controller with evolving structure
    • Angelov PP (2004) A fuzzy controller with evolving structure. Inf Sci 161(1-2):21-35.
    • (2004) Inf Sci , vol.161 , Issue.1-2 , pp. 21-35
    • Angelov, P.P.1
  • 4
    • 71449090241 scopus 로고    scopus 로고
    • Dissertation, Fakultät für Wirtschaftswissenschaften der Rheinisch-Westfälischen Technischen Hochschule, Aachen, Germany
    • Angstenberger L (2000) Dynamic Fuzzy Pattern Recognition. Dissertation, Fakultät für Wirtschaftswissenschaften der Rheinisch-Westfälischen Technischen Hochschule, Aachen, Germany.
    • (2000) Dynamic Fuzzy Pattern Recognition
    • Angstenberger, L.1
  • 11
    • 0030260017 scopus 로고    scopus 로고
    • A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
    • Frigui H, Krishnapuram R (1996) A robust algorithm for automatic extraction of an unknown number of clusters from noisy data. Pattern Recognit Lett 17:1223-1232.
    • (1996) Pattern Recognit Lett , vol.17 , pp. 1223-1232
    • Frigui, H.1    Krishnapuram, R.2
  • 12
    • 0031192257 scopus 로고    scopus 로고
    • Clustering by competitive agglomeration
    • Frigui H, Krishnapuram R (1997) Clustering by competitive agglomeration. Pattern Recognit 307:1109-1119.
    • (1997) Pattern Recognit , vol.307 , pp. 1109-1119
    • Frigui, H.1    Krishnapuram, R.2
  • 13
    • 0034187078 scopus 로고    scopus 로고
    • General fuzzy min-max neural network for clustering and classification
    • Gabrys B, Bargiela A (2000) General fuzzy min-max neural network for clustering and classification. IEEE Trans Neural Netw 11(3):769-783.
    • (2000) IEEE Trans Neural Netw , vol.11 , Issue.3 , pp. 769-783
    • Gabrys, B.1    Bargiela, A.2
  • 15
    • 77950860271 scopus 로고    scopus 로고
    • Evolving decision tree rule based system for audio stego anomalies detection based on Hausdorff distance statistics
    • Geetha S, Ishwarya N, Kamaraj N (2010) Evolving decision tree rule based system for audio stego anomalies detection based on Hausdorff distance statistics. Inf Sci.
    • (2010) Inf Sci
    • Geetha, S.1    Ishwarya, N.2    Kamaraj, N.3
  • 16
    • 0028484017 scopus 로고
    • Adaptive classification of myocardial electrogram waveforms
    • Gibb WJ, Auslander DM, Griffin JC (1994) Adaptive classification of myocardial electrogram waveforms. IEEE Trans Biomed Eng (41):804-808.
    • (1994) IEEE Trans Biomed Eng , Issue.41 , pp. 804-808
    • Gibb, W.J.1    Auslander, D.M.2    Griffin, J.C.3
  • 17
    • 77249134035 scopus 로고    scopus 로고
    • Dynamic hierarchical algorithms for document clustering
    • Gil-García R, Pons-Porrata A (2010) Dynamic hierarchical algorithms for document clustering. Pattern Recognit Lett 31(6):469-477.
    • (2010) Pattern Recognit Lett , vol.31 , Issue.6 , pp. 469-477
    • Gil-García, R.1    Pons-Porrata, A.2
  • 18
    • 0033556908 scopus 로고    scopus 로고
    • An on-line agglomerative clustering method for non-stationary data
    • Guedalia ID, London M, Werman M (1999) An on-line agglomerative clustering method for non-stationary data. Neural Comput 11(2):521-540.
    • (1999) Neural Comput , vol.11 , Issue.2 , pp. 521-540
    • Guedalia, I.D.1    London, M.2    Werman, M.3
  • 19
    • 39749086153 scopus 로고    scopus 로고
    • Using cooperative mobile agents to monitor distributed and dynamic environments
    • Ilarri S, Mena E, Illarramendi A (2008) Using cooperative mobile agents to monitor distributed and dynamic environments. Inf Sci 178(9):2105-2127.
    • (2008) Inf Sci , vol.178 , Issue.9 , pp. 2105-2127
    • Ilarri, S.1    Mena, E.2    Illarramendi, A.3
  • 27
    • 73249126966 scopus 로고    scopus 로고
    • A new monitoring design for univariate statistical quality control charts
    • (special issue on modelling uncertainty)
    • Nezhad MSF, Niaki STA (2010) A new monitoring design for univariate statistical quality control charts. Inf Sci 180(6) (special issue on modelling uncertainty, pp 1051-1059).
    • (2010) Inf Sci , vol.180 , Issue.6 , pp. 1051-1059
    • Nezhad, M.S.F.1    Niaki, S.T.A.2
  • 28
    • 2342625315 scopus 로고    scopus 로고
    • Adaptive K-NN for the detection of air pollutants with a sensor array
    • Roncaglia A, Elmi I, Dori L et al (2004) Adaptive K-NN for the detection of air pollutants with a sensor array. IEEE Sens J 4 (2).
    • (2004) IEEE Sens J , vol.4 , Issue.2
    • Roncaglia, A.1    Elmi, I.2    Dori, L.3
  • 35
    • 77549086087 scopus 로고    scopus 로고
    • A driver fatigue recognition model based on information fusion and dynamic Bayesian network
    • Yang G, Lin Y, Bhattacharya P (2010) A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Inf Sci 180(10):1942-1954.
    • (2010) Inf Sci , vol.180 , Issue.10 , pp. 1942-1954
    • Yang, G.1    Lin, Y.2    Bhattacharya, P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.