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Volumn , Issue , 2007, Pages 3759-3765

WAIRS: Improving classification accuracy by weighting attributes in the AIRS classifier

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL IMMUNE RECOGNITION SYSTEM (AIRS); COMPETITIVE CLASSIFIERS;

EID: 79955304363     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2007.4424960     Document Type: Conference Paper
Times cited : (15)

References (36)
  • 1
    • 0013359964 scopus 로고    scopus 로고
    • Masters Dissertation, Department of Computer Science, Mississippi State University, MS. USA
    • A. Watkins, "AIRS: A Resource Limited Artificial Immune Classifier", Masters Dissertation, Department of Computer Science, Mississippi State University, MS. USA, 2001.
    • (2001) AIRS: A Resource Limited Artificial Immune Classifier
    • Watkins, A.1
  • 5
    • 3543121725 scopus 로고    scopus 로고
    • Artificial Immune Recognition System (AIRS): An immune-inspired supervised learning algorithm
    • A. Watkins, et al., "Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm", Genetic Programming and Evolvable Machines, 5(3), pp. 291-317, 2004.
    • (2004) Genetic Programming and Evolvable Machines , vol.5 , Issue.3 , pp. 291-317
    • Watkins, A.1
  • 6
    • 4344594693 scopus 로고    scopus 로고
    • Non-euclidean distance measures in AIRS, an artificial immune classification system
    • J. Hamaker and L. Boggess, "Non-Euclidean Distance Measures in AIRS, an Artificial Immune Classification System" in 2004 Congress on Evolutionary Computation (CEC 2004), 2004, pp.1067-1073.
    • (2004) 2004 Congress on Evolutionary Computation (CEC 2004) , pp. 1067-1073
    • Hamaker, J.1    Boggess, L.2
  • 8
    • 0025725905 scopus 로고
    • Instance-based learning algorithms
    • D. W. Aha, et al., "Instance-Based Learning Algorithms", Machine Learning, 6(1), pp. 37-66, 1991.
    • (1991) Machine Learning , vol.6 , Issue.1 , pp. 37-66
    • Aha, D.W.1
  • 11
    • 85107848924 scopus 로고
    • Selecting typical instances in instance-based learning
    • Aberdeen, Scotland, UK
    • J. Zhang, "Selecting Typical Instances in Instance-Based Learning" in 9th International Conference on Machine Learning (ICML 2000), Aberdeen, Scotland, UK, 1992, pp.470-479.
    • (1992) 9th International Conference on Machine Learning (ICML 2000) , pp. 470-479
    • Zhang, J.1
  • 13
    • 0000217085 scopus 로고
    • Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
    • D. W. Aha, "Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms", International Journal of ManMachine Studies, 6(1), pp. 267-287, 1992.
    • (1992) International Journal of ManMachine Studies , vol.6 , Issue.1 , pp. 267-287
    • Aha, D.W.1
  • 14
    • 0031073477 scopus 로고    scopus 로고
    • A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms
    • D. Wettschereck, et al., "A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms", Artificial Intelligence Review, 11, pp. 273-314, 1997.
    • (1997) Artificial Intelligence Review , vol.11 , pp. 273-314
    • Wettschereck, D.1
  • 15
    • 0002949602 scopus 로고    scopus 로고
    • Genetic algorithms to learn feature weights for the nearest neighbor algorithm
    • G. Demiroz and H. A. Güvenir, "Genetic Algorithms to Learn Feature Weights for the Nearest Neighbor Algorithm" in BENELEARN-96, 1996, pp.117-126.
    • (1996) BENELEARN-96 , pp. 117-126
    • Demiroz, G.1    Güvenir, H.A.2
  • 17
    • 0002564447 scopus 로고
    • An experimental comparison of the nearest-neighbor and nearest-hyperrectangle algorithms
    • D. Wettschereck and T. G. Dietterich, "An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms", Machine Learning, 19(1), pp. 5-27, 1995.
    • (1995) Machine Learning , vol.19 , Issue.1 , pp. 5-27
    • Wettschereck, D.1    Dietterich, T.G.2
  • 18
    • 0031073476 scopus 로고    scopus 로고
    • Computing optimal attribute weight settings for nearest neighbour algorithms
    • C. X. Ling and H. Wang, "Computing Optimal Attribute Weight Settings for Nearest Neighbour Algorithms", Artificial Intelligence Review, 11, pp. 255-272, 1997.
    • (1997) Artificial Intelligence Review , vol.11 , pp. 255-272
    • Ling, C.X.1    Wang, H.2
  • 19
    • 84873990341 scopus 로고    scopus 로고
    • Re-visiting the foundations of artificial immune systems for data mining
    • To appear
    • A. A. Freitas and J. Timmis, "Re-visiting the Foundations of Artificial Immune Systems for Data Mining", To appear in IEEE Transactions on Evolutionary Computation, pp. 2006.
    • IEEE Transactions on Evolutionary Computation , pp. 2006
    • Freitas, A.A.1    Timmis, J.2
  • 21
    • 85061066913 scopus 로고
    • Selection of relevant features in machine learning
    • New Orleans
    • P. Langley, "Selection of Relevant features in machine Learning" in AAAI Fall Symposium on Relevance, New Orleans, 1994, pp.1-5.
    • (1994) AAAI Fall Symposium on Relevance , pp. 1-5
    • Langley, P.1
  • 22
    • 39549109168 scopus 로고
    • Trading MIPS and memory for knowledge engineering
    • R. H. Creecy, et al., "Trading MIPS and Memory for Knowledge Engineering", Communications of the ACM, 35, pp. 48-64, 1992.
    • (1992) Communications of the ACM , vol.35 , pp. 48-64
    • Creecy, R.H.1
  • 23
    • 0022909661 scopus 로고
    • Toward memory-based reasoning
    • C. Stanfill and D. Waltz, "Toward Memory-based reasoning", Communications of the ACM, 29(12), pp. 1213-1228, 1986.
    • (1986) Communications of the ACM , vol.29 , Issue.12 , pp. 1213-1228
    • Stanfill, C.1    Waltz, D.2
  • 25
    • 18944389149 scopus 로고    scopus 로고
    • Data reduction: Discretization of numerical attributes
    • W. Klösgen and J. M. Zytkow Ed. Oxford University Press
    • J. W. Grzymala-Busse, "Data reduction: discretization of numerical attributes", in Handbook of Data Mining and Knowledge Discovery, W. Klösgen and J. M. Zytkow Ed. Oxford University Press, 2002, pp. 218-225.
    • (2002) Handbook of Data Mining and Knowledge Discovery , pp. 218-225
    • Grzymala-Busse, J.W.1
  • 26
    • 85139983802 scopus 로고
    • Supervised and unsupervised discretization of continuous features
    • San Francisco, USA
    • J. Dougherty, et al., "Supervised and Unsupervised Discretization of Continuous Features" in 12th International Conference on Machine Learning, San Francisco, USA, 1995
    • (1995) 12th International Conference on Machine Learning
    • Dougherty, J.1
  • 27
    • 84947714091 scopus 로고    scopus 로고
    • Multi-interval discretization methods for decision tree learning
    • P. Perner and S. Trautzsch, "Multi-Interval Discretization Methods for Decision Tree Learning" in SSPR/SPR, 1998, pp.475-482.
    • (1998) SSPR/SPR , pp. 475-482
    • Perner, P.1    Trautzsch, S.2
  • 31
    • 0031070033 scopus 로고    scopus 로고
    • Discretisation in lazy learning algorithms
    • K. M. Ting, "Discretisation in Lazy Learning Algorithms", Artificial Intelligence Review, 11, pp. 157-174, 1997.
    • (1997) Artificial Intelligence Review , vol.11 , pp. 157-174
    • Ting, K.M.1
  • 36
    • 84901426745 scopus 로고    scopus 로고
    • A new classifier based on resource limited artificial immune systems
    • Honolulu, USA
    • A. Watkins and L. Boggess, "A New Classifier Based on Resource Limited Artificial Immune Systems" in Congress on Evolutionary Computation (CEC 2002), Honolulu, USA, 2002, pp.1546-1551.
    • (2002) Congress on Evolutionary Computation (CEC 2002) , pp. 1546-1551
    • Watkins, A.1    Boggess, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.