1. Kahramanlı H., and Allahverdi N., “Rule
Extraction from Trained Adaptime Neural
Networks Using ArtificialİmmuneSystems”,
Expert Systems With Applications, Sayı 36,
pp, 1513-1522, 2009.
2. Gallant, S. I., “Connection Expert
Systems”,Communications of the ACM, 31(2),
pp.152–169, 1998.
3. Towell, G. G., and Shavlik, J., “Extracting
Refined Rules From Knowledge-Based Neural
Networks”. Machine Learning, 13, pp.71–101,
1993.
4. Lu, H., Setiono, R., and Liu, H., “Effective Data
Mining Using Neural Networks”,IEEE
Transactions on Knowledge and Data
Engineering, 8(6), pp.957–961, 1996.
5. Keedwell, E., Narayanan, A., and Savic, D.,
“Evolving rules from neural networks trained on
Sınıflandırma Kurallarının Çıkarımı İçin Etkin ve Hassas Yeni Bir Yaklaşım M. Köklü ve ark.
Gazi Üniv. Müh. Mim. Fak. Der. Cilt 29, No 3, 2014 485
continuous data”,Evolutionary Computation
Congress,Proceedings Of The Congress On
Evolutionary Computation, 1, pp.639-645, 2000a.
6. Keedwell, E., Narayanan, A., and Savic, D.,
“Creating Rules From Trained Neural Networks
Using Genetic Algorithms”International
Journal of Computers Systeming Signals
(IJCSS), 1(1), pp.30–42, 2000b.
7. Aliev R.A., Aliev R.R., Guirimov B. and Uyar
K., “Dynamic Data Mining Technique for Rules
Extraction in a Process of Battery Charging”,
Applied Soft Computing, 8, pp. 1252–1258,
2008.
8. Ang J.H., Tan K.C. and Mamun A.A., “An
Evolutionary Memetic Algorithm For Rule
Extraction”, Expert Systems with Applications,
Volume 37, 2, pp.1302-1315, 2010.
9. Papageorgiou E.I., “A New Methodology for
Decisions in Medical Informatics Using Fuzzy
Cognitive Maps Based on Fuzzy Rule-Extraction
Techniques”, Applied Soft Computing, 11,
pp.500–513, 2011.
10. Rodríguez M., Escalante D. M., and Peregrín A.,
“Efficient Distributed Genetic Algorithm for Rule
Extraction”, Applied Soft Computing, Vol.11,
1, pp. 733-743,2011.
11. Sarkar, B.K., Sana S.S. and Chaudhuri K., “A
Genetic Algorithm-Based Rule Extraction
System”,Applied Soft Computing 12,pp.238–
254, 2012.
12. Özbakır L., Baykasoğlu A. and Kulluk S., “A
Soft Computing-Based Approach for Integrated
Training and Rule Extraction From Artificial
Neural Networks: DIFACONN-Miner”, Applied
Soft Computing 10,pp.304–317, 2010.
13. Baykasoglu A., Saltabaş A., Taşan A.S. and
Subulan K., “Yapay Bağışıklık Sisteminin Çoklu
Etmen Benzetim Ortamında Realize Edilmesi ve
Gezgin Satıcı Problemine Uygulanması” Gazi
Üniversitesi Mühendislik Mimarlık Fakültesi
Dergisi, Cilt 27, No 4, 901-909, 2012.
14. Seredynski, F., and Bouvry, P., “Anomaly
Detection in TCP/IP Networks Using Immune
Systems Paradigm”,Computer
Communications, 30, pp.740–749, 2007.
15. Kalinli, A., and Karaboga, N., “Artificial Immune
Algorithm for IIR Filter Design”,Engineering
Applications of Artificial Intelligence, 18,
pp.919–929, 2005.
16. Musilek, P., Lau, A., Reformat, M., & Wyard-
Scott, L., “Immune Programming”,Information
Sciences, 176, pp.972–1002, 2006.
17. Kumar, A., Prakash, A., Shankar, R., & Tiwari,
M. K., “Psychoclonal Algorithm Based Approach
to Solve Continuous Flow Shop Scheduling
Problem”,Expert System with Applications, 31,
pp.504–514, 2006.
18. De Castro, L. N., & Timmis, J., “Artificial
Immune Systems: ANew Computational
Intelligence Approach”. UK: Springer, 2002.
19. Hart, E. and Timmis, J., “Application Areas of
AIS: The past, The Present and The Future”,
International Conference on Artificial
Immune Systems (ICARIS), Canada, LNCS
3627, pp. 483-497, 2005.
20. Timmis, J., Hone, A., Stibor, T. and Clarck, E.,
“Theoretical advances in artficial immune
systems”, Theoretical Computer Science, 403,
pp. 11-32, 2008.
21. Brownlee, J., “Clonal Selection Theory &
CLONALG the Clonal Selection Classification
Algorithm (CSCA)”, Tecnical Report, 2005.
22. Parpinelli, R.S., Lopes, H.S., Freitas, A.A., “An
Ant Colony Based System For Data Mining:
Applications To Medical Data”,Proceedings of
the Genetic and Evolutionary Computation
Conference, San Francisco, California, pp.791–
798, 2001.
23. Frank, A. and Asuncion, A., “UCI Machine
LearningRepository”,[http://archive.ics.uci.edu/m
l], Irvine, CA: University of California, School
of Information and Computer Science, Last
accessed:February, 2011.
24. Clark D., Schreter Z. and Adams A., "A
Quantitative Comparison of Dystal and
Backpropagation", Submitted to the Australian
Conference on Neural Networks,1996.
25. Rijnbeek P.R. and Kors J.A., “Finding a Short
and Accurate Decision Rule in Disjunctive
Normal Form by Exhaustive Search”, Machine
Learning, 80: 33–62, DOI 10.1007/s10994-010-
5168-9, 2010.
26. Nojima Y., Ishibuchi H. And Kuwajima I.,
“Parallel Distributed Genetic Fuzzy Rule
Selection (FDGFRS)”, Soft Computing, 13,
pp.511–519, 2009.
27. Ful X. and Wang L., “A Rule Extraction System
with Class-Dependent Features”, Studies in
Fuzziness and Soft Computing, Volume
163/2005, pp.79-99, DOI: 10.1007/3-540-32358-
9_5, 2005.
28. Özbakır, L., Baykasoğlu, A., Kulluk, S., Yapıcı,
H., “TACO-miner: An Ant Colony Based
Algorithm for Rule Extraction from Trained
Neural Networks”, Expert Systems with
Applications, 36, 12295-12305, 2009.
29. Özbakır, L.,Delice,Y., “Exploring
comprehensible classification rules from trained
neural Networks integrated with a time-varying
binary particle swarm optimizer”, Engineering
Applications of Artificial Intelligence, 24, 491-
500, 2011.
30. Browne C., Duntsch I. and Gediga G., “IRIS
Revisited: A Comparison Of Discriminant and
Enhanced Rough Set Data Analysis”. In: L.
Polkowski and A. Skowron, eds. Rough Sets in
Knowledge Discovery, vol. 2. Physica Verlag,
Heidelberg, 345-368, 1998.
31. Weiss S.M. and Kapouleas, I., "An Empirical
Comparison of Pattern Recognition, Neural Nets
M. Köklü ve ark. Sınıflandırma Kurallarının Çıkarımı İçin Etkin ve Hassas Yeni Bir Yaklaşım
486 Gazi Üniv. Müh. Mim. Fak. Der. Cilt 29, No 3, 2014
and Machine Learning Classification Methods",
in: J.W. Shavlik and T.G. Dietterich, Readings in
Machine Learning, Morgan Kauffman Publ, CA
1990.
32. Duch W, Adamczak R, Grąbczewski K., “A New
Methodology of Extraction, Optimization and
Application of Crisp and Fuzzy Logical Rules”.
IEEE Transactions on Neural Networks, 12,
pp.277-306, 2001.
33. Nauck D., Nauck U. and Kruse R., “Generating
Classification Rules with the Neuro-Fuzzy
System NEFCLASS”,Proc. Biennial Conf. of
the North American Fuzzy Information
Processing Society (NAFIPS'96), Berkeley,
1996.
34. Pal N. R. and Chakraborty S., “Fuzzy Rule
Extraction From ID3-Type Decision Trees for
Real Data”, IEEE Trans. Syst., Man, Cybern. B.
31 pp. 745-754, 2001.
35. Wang S., Wu G. and Pan J., “A Hybrid Rule
Extraction Method Using Rough Sets and Neural
Networks”, Lecture Notes in Computer
Science, 2, 4492, pp. 352–361, 2007.
36. Castellano G., Fanelli A. M., and Mencar C., “An
empirical risk functional to improve learning in a
neuro-fuzzy Classifier”,IEEE Trans. Syst., B. 34
pp. 725-731, 2004.
37. Kim D.W., Park J.B., and Joo Y.H., “Design of
Fuzzy Rule-Based Classifier: Pruning and
Learning”, Fuzzy Systems and Knowledge
Discovery Lecture Notes in Computer Science,
3613, pp. 416–425, Springer, 2005.
38. Pappa G.L. and Freitas A. A., “Evolving Rule
Induction Algorithms with Multi-Objective
Grammar-Based Genetic Programming”,
Knowledge and Information Systems, 19,
pp.283–309. DOI 10.1007/s10115-008-0171-1,
2009.
39. De Castro, L. N.,, "Learning and Optimization
Using the Clonal Selection Principle",IEEE
Transactions On Evolutionary Computation,
Vol. 6, no. 3, June, 2002.
40. C 4.5 Lecture Notes, [http://www.sts.tuharburg.
de/teaching/ss-09/ml-sose-09/03-
Decision-Tree-c45.pdf], Hamburg University of
Technology, Lecture Notes in Electronic
Science,Last accessed,: Semptember, 2013.
41. Huysmans J., Baesens B. and Vanthienen J,
“Using Rule Extraction to Improve the
Comprehensibility of Predictive Models”, Open
Access publications from Katholieke
Universiteit Leuven with number
urn:hdl:123456789/121060, KBI-0612, 2006.7
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