Generic methods for multi-criteria evaluation

Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130

Volumn 2, Issue , 2008, Pages 541-546

  Lavesson, Niklas ^a   Davidsson, Paul ^a  

^a BLEKINGE INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS;

ALGORITHM EVALUATION; DATA MINING ALGORITHM; EVALUATION FUNCTION; EVALUATION METRICS; GENERIC METHOD; MULTI-CRITERIA; MULTI-CRITERIA EVALUATION; REAL-WORLD PROBLEM;

DATA MINING;

EID: 52649164619     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972788.49     Document Type: Conference Paper

References (9)

1. A. Andersson, P. Davidsson, and J. Linden. Measurebased Classifier Performance Evaluation. Pattern Recognition Letters, 20(11-13):1165-1173, 1999.
2. M. Boldt, A. Jacobsson, N. Lavesson, and P. Davidsson. Automated Spyware Detection Using End User License Agreements. In 2nd International Conference on Information Security and Assurance, Busan, Korea, 2008.
3. R. Caruana and A. Niculescu-Mizil. An Empirical Comparison of Supervised Learning Algorithms. In 23rd International Conference on Machine Learning, pages 161-168, 2006.
4. A. Freitas. A Critical Review of Multi-Objective Optimization in Data Mining: A Position Paper. ACM SIGKDD Explorations Newsletter, 6(2):77-86, 2004.
5. N. Lavesson and P. Davidsson. Quantifying the Impact of Learning Algorithm Parameter Tuning. In Proceedings of the 21st AAAI National Conference on Artificial Intelligence, pages 395-400. AAAI Press, 2006.
6. G. Nakhaeizadeh and A. Schnabl. Development of Multi-Criteria Metrics for Evaluation of Data Mining Algorithms. In 3rd International Conference on Knowledge Discovery and Data Mining, pages 37-42, Newport Beach, CA, USA, 1997.
7. F. Provost, T. Fawcett, and R. Kohavi. The Case Against Accuracy Estimation for Comparing Induction Algorithms. In 15th International Conference on Machine Learning, pages 445-453, Madison, WI, USA, 1998. Morgan Kaufmann.
8. C. Soares, J. Costa, and P. Brazdil. A simple and intuitive measure for multi-criteria evaluation of classification algorithms. In ECML2000 Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, pages 87-96, Barcelona, Spain, 2000. Springer Science.
9. D. H. Wolpert. Off-training-set Error and A Priori Distinctions between Learning Algorithms. Technical Report SFI-TR 95-01-003, Sante Fe Institute, Santa Fe, NM, USA, 1995.