Cost-sensitive splitting and selection method for medical decision support system

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7435 LNCS, Issue , 2012, Pages 850-857

  Jackowski, Konrad ^a   Krawczyk, Bartosz ^a   Woźniak, Michał ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

clustering and selection; cost sensitive classification; evolutionary algorithm; feature selection; machine learning; multiple classifier system

Indexed keywords

ADAPTIVE SPLITTING; BENCHMARK DATASETS; CLASSIFICATION ACCURACY; CLINICAL DECISION SUPPORT SYSTEMS; CLUSTERING AND SELECTION; COMPUTER EXPERIMENT; COST CONSTRAINTS; COST-SENSITIVE; COST-SENSITIVE CLASSIFICATION; FEATURE SPACE; MEDICAL DECISION SUPPORT SYSTEM; MULTIPLE CLASSIFIER SYSTEMS; SELECTION METHODS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS; EVOLUTIONARY ALGORITHMS; FEATURE EXTRACTION; LEARNING SYSTEMS;

COSTS;

EID: 84865030862     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-32639-4_101     Document Type: Conference Paper

References (18)

1. Alpaydin, E.: Introduction to Machine Learning, 2nd edn. The MIT Press (2010)
2. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)
3. Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
4. Jackowski, K., Wozniak, M.: Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas. Pattern Analysis and Applications 12(4), 415-425 (2009)
5. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 264-323 (1999)
6. Krawczyk, B., Woźniak, M.: Designing Cost-Sensitive Ensemble - Genetic Approach. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 3. AISC, vol. 102, pp. 227-234. Springer, Heidelberg (2011)
7. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience (2004)
8. Lirov, Y., Yue, O.-C.: Automated network troubleshooting knowledge acquisition. Applied Intelligence 1, 121-132 (1991)
9. Michalewicz, Z.: Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, London (1996)
10. Núñez, M.: The use of background knowledge in decision tree induction. Mach. Learn. 6(3), 231-250 (1991)
11. Núñez, M.: Economic induction: A case study. In: EWSL, pp. 139-145 (1988)
12. Penar, W., Wozniak, M.: Cost-sensitive methods of constructing hierarchical classifiers. Expert Systems 27(3), 146-155 (2010)
13. Peng, Y.H., Huang, Q., Jiang, P., Jiang, J.: Cost-Sensitive Ensemble of Support Vector Machines for Effective Detection of Microcalcification in Breast Cancer Diagnosis. In: Wang, L., Jin, Y. (eds.) FSKD 2005, Part II. LNCS (LNAI), vol. 3614, pp. 483-493. Springer, Heidelberg (2005)
14. Tan, M., Schlimmer, J.C.: Cost-sensitive concept learning of sensor use in approach and recognition. In: Proceedings of the Sixth International Workshop on Machine Learning, pp. 392-395. Morgan Kaufmann Publishers Inc., San Francisco (1989)
15. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)
16. Verdenius, F.: A method for inductive cost optimization. In: Kodratoff, Y. (ed.) EWSL 1991. LNCS, vol. 482, pp. 179-191. Springer, Heidelberg (1991)
17. Wozniak, M., Krawczyk, B.: Combined classifier based on feature space partitioning. Journal of Applied Mathematics and Computer Science 22(4) (in press, to appear, 2012)
18. Wozniak, M., Zmyslony, M.: Combining classifiers using trained fuser - analytical and experimental results. Neural Network World 13(7), 925-934 (2010)