kabaca
sonar sinyalleri Veri seti literatüre Gorman ve Sejnowski (1988) Veri setinde 111 tane sinyal metal silindirlerden ve 97 tane sinyal sonar verisinden elde
. Her desen 0.0-
KAA- F çekirdek fonksiyonu, SSA-DVM mod KAA-DVM SSA- KAA-DVM ve SSA- -0.7914, 0.8557-0.8516, 0.8756-0.8731 -
Çizelge 4.12. Sonar veri seti üzeri
Çekirdek
Fonksiyonu KAA-DVM SSA-DVM
Lineer 75.48 ± 11.65 75.00 ± 7.62 0.7350 ± 0.17 0.7196 ± 0.14 Seçicilik 0.7740 ± 0.10 0.7765 ± 0.09 AUC 0.7968 ± 0.03 0.7865 ± 0.03 Polinom D=1 74.04 ± 9.32 75.00 ± 7.63 0.7168 ± 0.18 0.7196 ± 0.14 Seçicilik 0.7682 ± 0.12 0.7765 ± 0.09 AUC 0.7990 ± 0.04 0.7865 ± 0.03 Polinom D=2 87.02 ± 4.50 85.10 ± 7.72 0.8484 ± 0.10 0.8342 ± 0.10 Seçicilik 0.9068 ± 0.08 0.8766 ± 0.10 AUC 0.9116 ± 0.06 0.9116 ± 0.06 Polinom D=3 87.02 ± 4.50 87.50 ± 10.59 0.8484 ± 0.10 0.8496 ± 0.15 Seçicilik 0.9068 ± 0.08 0.9075 ± 0.09 AUC 0.9116 ± 0.06 0.9284 ± 0.06 RBF 88.46 ± 5.62 88.50 ± 6.80 0.8550 ± 0.10 0.8230 ± 0.14 Seçicilik 0.9229 ± 0.06 0.9320 ± 0.07 AUC 0.9591 ± 0.06 0.9524 ± 0.04 - - kutu grafikleri görülmektedir. -DVM ve SSA-
grafikleri görülmektedir. Her iki modelde de polinom (d=1) ve lineer çekirdek
model
-DVM sonar veri seti üzerindeki
-DVM ve SSA-
KAA- RBF çekidek
linee
KAA-
-
Çizelge 4.13
Algoritma Parkinson Veri Seti Karga Arama Alg. 88.46 ± 5.62
Salp Sürüsü Alg. 88.50 ± 6.80 GOA 88.55 ± 8.01 MVO 88.50 ± 6.14 GA 87.52 ± 8.57 PSO 87.98 ± 5.34 GWO 88.50 ± 5.93 FA 88.45 ± 3.85 BA 88.02 ± 7.48 CSA 85.10 ± 6.88
4.5. Çok
o harf veri
(Aeberhard ve ark., 1994)
analiz verilerini içerir. 13 fark Cam veri seti
(Evett ve Spiehler, 1987) Amerika Devletleri (ABD) adli bilimler ofisinden Kriminolojik vakalarda
Ünlü harf veri setinin (Niranjan ve Fallside, 1990)
Ünlüler 0- - Çizelge 4.14. K SONAR CAM ÜNLÜ HARF KAA 97.22 78.25 94.87 88.46 97.78 67.77 98.35 SSA 96.72 77.99 95.38 88.50 97.19 66.64 99.62 GA 96.19 81.50 - 98.00 - - 99.30 GS 95.30 77.3 - 87 - - 99.95 PSO 97.95 80.19 - 88.32 - - 99.27 GOA 97.23 76.0 94.95 88.55 97.77 70.06 99.80 BA 96.80 83.50 - 96.30 97.60 83.80 - FOA 96.90 77.46 96.90 - - - - SSO - 71.12 - 69.42 - 59.99 - IACO 96.93 86.98 - - 92.70 63.80 -
parametre optimizasyonu Çizelgede görülmektedir
ki KAA-DVM ve SSA-DVM modelleri literatürdeki
vermektedir. Çizelge
Çizelge gösterilen
KAA ve SSA ye
görülmektedir. Sonar veri setinde
Cam veri
setinde BA 67.77 ve 66.64
Çizelge 4.15. Literatür
Veri Seti Referans
KAA-DVM 100 7
SSA-DVM 100 7
GA-DVM 600 11 (Huang ve Wang, 2006)
GS-DVM 600 11 (Huang ve Wang, 2006)
PSO-DVM 250 17 (Lin ve ark., 2008)
GOA-DVM 200 18 (Aljarah ve ark., 2018)
BA-DVM 20 9 (Tharwat ve ark., 2017)
FOA-DVM 250 4 (Shen ve ark., 2016)
SSO-DVM 200 10 (Pereira ve ark., 2014)
IACO-DVM 500 9 (Chen ve Tian, 2016)
4.6
Bu bölüme
DVM parametre optimizasyonu
an literatür
çizelgeler
(d=1,2,3) ve RBF içi Deney sonunda optimize edilen parametrelerin
(Aljarah ve ark., 2018) yer Modellerin ç
Ancak bulunma
literatüre giren iki meta-sezgisel algoritma olan Karga Arama
KAA 2016 ve meta- . Bu algoritma
bulunabilir. Bu süreçten ilham alarak modellenen KAA,
SSA -sezgisel
dahi az iken, yazarlar
kirdek fonksiyonu (lineer, polinom (d=1,2,3), RBF) Çekirdek
lere
hesaplama
çekirdek fonksiyonudur. neer
eneyler
göstermektedir ki RBF çekirdek fonksiyonu veri
daha stabil sonuçlar vermektedir. Lineer çekirdek fonksiyonu ve polinom (d=1) çekirdek fonksiyonu
gerek rakamsal sonuçlarda gerekse grafiklerde çok benzer iki çekirdek fonksiyonu
Pa
Bu veri setlerinde RBF çekirdek fonksiyonu
uygun
polinom çekirdek fonksiyonunun d>2 için -
uyuma (overfit) sebep o Lineer çekirdek fonksiyonunun
(96.72)
veri setinde iyi sonuç vermesi di
nde hem her çekirdek fonksiyonu için hem de ortalama olarak, KAA
KA RBF çekirdek
parametre optimizasyonu modeli -
ki KAA-DVM ve SSA-DVM
alternatifler olarak sunulabilirler. da önerilen modellerin DVM parametrelerini . DVM parametre optimizasyonunu algoritma
metodolojiler -DVM (TWSVM) modeli her in paralel olmayan
hesaplama
KAYNAKLAR
Aeberhard, S., Coomans, D. ve de Vel, O., 1994, Comparative analysis of statistical pattern recognition methods in high dimensional settings, Pattern Recognition, 27 (8), 1065-1077.
Alatas, B., 2011, ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization, Expert Systems with Applications, 38 (10), 13170-13180. Ali, S. ve Smith-Miles, K., 2003, Automatic parameter selection for polynomial kernel,
p.
Aljarah, I., Al-Zoubi, A. M., Faris, H., Hassonah, M. A., Mirjalili, S. ve Saadeh, H., 2018, Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm, Cognitive Computation, 10 (3), 478- 495.
Anderson, P. A. ve Bone, Q., 1980, Communication between individuals in salp chains. II. Physiology, Proceedings of the Royal Society of London. Series B. Biological Sciences, 210 (1181), 559.
Ao, H., Junsheng, C., Yang, Y. ve Truong, T., 2013, The support vector machine parameter optimization method based on artificial chemical reaction optimization algorithm and its application to roller bearing fault diagnosis, p.
Askarzadeh, A., 2016, A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, Computers & Structures, 169, 1- 12.
Bamakan, S. M. H., Wang, H. ve Ravasan, A. Z., 2016, Parameters Optimization for Nonparallel Support Vector Machine by Particle Swarm Optimization, Procedia Computer Science, 91, 482-491.
Bian, X.-Q., Zhang, Q., Zhang, L. ve Chen, J., 2017, A grey wolf optimizer-based support vector machine for the solubility of aromatic compounds in supercritical carbon dioxide, Chemical Engineering Research and Design, 123, 284-294.
Birattari, M., Paquete, L., Stützle, T. ve Varrentrapp, K., 2001, Classification of metaheuristics and design of experiments for the analysis of components, Teknik Rapor, AIDA-01-05.
Blum, C. ve Roli, A., 2003, Metaheuristics in combinatorial optimization: Overview and conceptual comparison, ACM Comput. Surv., 35 (3), 268-308.
Borges, L., 2015, Analysis of the Wisconsin Breast Cancer Dataset and Machine Learning for Breast Cancer Detection, p.
Boussaïd, I., Lepagnot, J. ve Siarry, P., 2013, A survey on optimization metaheuristics, Information Sciences, 237, 82-117.
Chang, C.-C. ve Lin, C.-J., 2011, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol., 2 (3), 1-27.
Chapelle, O., Vapnik, V., Bousquet, O. ve Mukherjee, S., 2002, Choosing Multiple Parameters for Support Vector Machines, Machine Learning, 46 (1), 131-159. Chen, H.-L., Yang, B., Wang, G., Liu, J., Xu, X., Wang, S.-J. ve Liu, D.-Y., 2011a, A
novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method, Knowledge-Based Systems, 24 (8), 1348-1359.
Chen, J.-L., Liu, H.-B., Wu, W. ve Xie, D.-T., 2011b, Estimation of monthly solar radiation from measured temperatures using support vector machines A case study, Renewable Energy, 36 (1), 413-420.
Chen, W. ve Tian, Y., 2016, Parameter Optimization of SVM Based on Improved ACO for Data Classification.
Chou, J.-S., Cheng, M.-Y., Wu, Y.-W. ve Pham, A.-D., 2014, Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification, Expert Systems with Applications, 41 (8), 3955-3964.
Cortes, C. ve Vapnik, V., 1995, Support-Vector Networks, Machine Learning, 20 (3), 273-297.
Ding, S., An, Y., Zhang, X., Wu, F. ve Xue, Y., 2017, Wavelet twin support vector machines based on glowworm swarm optimization, Neurocomputing, 225, 157- 163.
Dong, H. ve Jian, G., 2015, Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm, Cybern. Inf. Technol., 15 (3), 140-149.
Dorigo, M., Birattari, M. ve Stutzle, T., 2006, Ant colony optimization, IEEE Computational Intelligence Magazine, 1 (4), 28-39.
Dua, D., Karra Taniskidou, E., 2017, UCI Machine Learning Repository Irvine, CA: University of California, School of Information and Computer Science.
Evett, I. W. ve Spiehler, E., 1987, Rule induction in forensic science, KBS in Goverment, 107-118.
Fawcett, T., 2004, ROC graphs: Notes and practical considerations for researchers, Machine Learning, 31 (1), 1-38.
Fister jr, I., Yang, X.-S., Fister, I., Brest, J. ve Fister, D., 2013, A Brief Review of Nature- Inspired Algorithms for Optimization, p.
Friedrichs, F. ve Igel, C., 2005, Evolutionary tuning of multiple SVM parameters, Neurocomput., 64, 107-117.
Glover, F., 1986, Future paths for integer programming and links to artificial intelligence, Computers & Operations Research, 13 (5), 533-549.
Gorman, R. P. ve Sejnowski, T. J., 1988, Analysis of hidden units in a layered network trained to classify sonar targets, Neural Networks, 1 (1), 75-89.
Gumus, E., Kilic, N., Sertbas, A. ve Ucan, O. N., 2010, Evaluation of face recognition techniques using PCA, wavelets and SVM, Expert Systems with Applications, 37 (9), 6404-6408.
H Wolberg, W. ve L Mangasarian, O., 1991, Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology, p.
Hefny, H. A. ve Azab, S. S., 2010, Chaotic particle swarm optimization, 2010 The 7th International Conference on Informatics and Systems (INFOS), 1-8.
Holland, J. H., 1992, Adaptation in natural and artificial systems, MIT Press, p.
Hu, J., Zeng, X. ve Xiao, J., 2010, Artificial Fish School Algorithm for Function Optimization, 2010 2nd International Conference on Information Engineering and Computer Science, 1-4.
Huang, C.-L. ve Wang, C.-J., 2006, A GA-based feature selection and parameters optimizationfor support vector machines, Expert Systems with Applications, 31 (2), 231-240.
James, G., Witten, D., Hastie, T. ve Tibshirani, R., 2014, An Introduction to Statistical Learning: with Applications in R, Springer Publishing Company, Incorporated, p. Jean-Pierre, D., Florent, B., Hairong, X., Annick, P. ve Botao, F., 2007, Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design, Current Computer-Aided Drug Design, 3 (4), 263-289.
Kennedy, J. ve Eberhart, R., 1995, Particle swarm optimization, Neural Networks, 1995. Proceedings., IEEE International Conference on, 1942-1948 vol.1944.
Krishnanand, K. ve Ghose, D., 2005, Detection of multiple source locations using a glowworm metaphor with applications to collective robotics, Swarm intelligence symposium, 2005. SIS 2005. Proceedings 2005 IEEE, 84-91.
LeCun, Y., Cortes, C. ve Burges, C. J. C., 2018, THE MNIST DATABASE of handwritten digits.
Li, F. ve Aggarwal, R. K., 2000, Fast and accurate power dispatch using a relaxed genetic algorithm and a local gradient technique, Expert Systems with Applications, 19 (3), 159-165.
Li, X. Z. ve Kong, J. M., 2014, Application of GA SVM method with parameter optimization for landslide development prediction, Nat. Hazards Earth Syst. Sci., 14 (3), 525-533.
Lin, K.-C., Chen, S.-Y. ve Hung, J. C., 2015, Feature selection and parameter optimization of support vector machines based on modified artificial fish swarm algorithms, Mathematical Problems in Engineering, 2015.
Lin, S.-W., Ying, K.-C., Chen, S.-C. ve Lee, Z.-J., 2008, Particle swarm optimization for parameter determination and feature selection of support vector machines, Expert Systems with Applications, 35 (4), 1817-1824.
Little, M. A., McSharry, P. E., Roberts, S. J., Costello, D. A. ve Moroz, I. M., 2007, Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection, BioMedical Engineering OnLine, 6 (1), 23.
Madin, L. P., 1990, Aspects of jet propulsion in salps, Canadian Journal of Zoology, 68 (4), 765-777.
Mangasarian, O. L., Street, W. N. ve Wolberg, W. H., 1995, Breast Cancer Diagnosis and Prognosis Via Linear Programming, Operations Research, 43 (4), 570-577. Miettinen, K., 1999, Evolutionary Algorithms in Engineering and Computer Science:
Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, GE, John Wiley \\& Sons, Inc., p.
Mirjalili, S., Mirjalili, S. M. ve Lewis, A., 2014, Grey Wolf Optimizer, Advances in Engineering Software, 69, 46-61.
Mirjalili, S., 2016, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications, 27 (4), 1053-1073.
Mirjalili, S. ve Lewis, A., 2016, The Whale Optimization Algorithm, Advances in Engineering Software, 95, 51-67.
Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H. ve Mirjalili, S. M., 2017, Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Advances in Engineering Software, 114, 163-191.
Nasraoui, O., 2008, Web data mining: exploring hyperlinks, contents, and usage data, SIGKDD Explor. Newsl., 10 (2), 23-25.
NEFSC, 2013, "Greetings from the nightwatch." Field Fresh
https://nefsc.wordpress.com/2013/08/07/greetings-from-the-nightwatch/ 2018.
Niranjan, M. ve Fallside, F., 1990, Neural networks and radial basis functions in classifying static speech patterns, Computer Speech & Language, 4 (3), 275-289. Nocedal, J. ve Wright, S. J., 2006, Numerical optimization 2nd, Springer.
Pai, P.-F., Hsu, M.-F. ve Lin, L., 2014, Enhancing decisions with life cycle analysis for risk management, Neural Computing and Applications, 24 (7), 1717-1724. Pereira, D. R., Pazoti, M. A., Pereira, L. A. M. ve Papa, J. P., 2014, A social-spider
optimization approach for support vector machines parameters tuning, 2014 IEEE Symposium on Swarm Intelligence, 1-6.
Pereira, D. R., Pazoti, M. A., Pereira, L. A. M., Rodrigues, D., Ramos, C. O., Souza, A. N. ve Papa, J. P., 2016, Social-Spider Optimization-based Support Vector Machines applied for energy theft detection, Computers & Electrical Engineering, 49, 25-38.
Pereira, L., Papa, J. ve Souza, A., 2013, Harmony search applied for support vector machines training optimization, p.
Rincon, P., 2005, Crows and jays top bird IQ scale
http://news.bbc.co.uk/2/hi/science/nature/4286965.stm: [15.03.2018].
Sai, L. ve Huajing, F., 2017, A WOA-based algorithm for parameter optimization of support vector regression and its application to condition prognostics, 2017 36th Chinese Control Conference (CCC), 7345-7350.
Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B. ve Liu, D., 2016, Evolving support vector machines using fruit fly optimization for medical data classification, Knowledge-Based Systems, 96, 61-75.
Spackman, K. A., 1989, Signal detection theory: valuable tools for evaluating inductive learning. Proceedings of the sixth international workshop on Machine learning. Ithaca, New York, USA, Morgan Kaufmann Publishers Inc.: 160-163.
Stadler, W., 2013, Multicriteria Optimization in Engineering and in the Sciences, Springer Science & Business Media, p.
Subasi, A. ve Ismail Gursoy, M., 2010, EEG signal classification using PCA, ICA, LDA and support vector machines, Expert Systems with Applications, 37 (12), 8659- 8666.
Subasi, A., 2013, Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders, Computers in Biology and Medicine, 43 (5), 576-586. Tharwat, A., Hassanien, A. E. ve Elnaghi, B. E., 2017, A BA-based algorithm for parameter optimization of Support Vector Machine, Pattern Recognition Letters, 93, 13-22.
Tharwat, A., Gabel, T. ve Hassanien, A. E., 2018, Parameter Optimization of Support Vector Machine Using Dragonfly Algorithm, Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017, Cham, 309- 319.
Tharwat, A. ve Hassanien, A. E., 2018, Chaotic antlion algorithm for parameter optimization of support vector machine, Applied Intelligence, 48 (3), 670-686. Tuba, E., Mrkela, L. ve Tuba, M., 2016, Support vector machine parameter tuning using
firefly algorithm, p.
Tuba, E., Ribic, I., Capor-Hrosik, R. ve Tuba, M., 2017, Support Vector Machine Optimized by Elephant Herding Algorithm for Erythemato-Squamous Diseases Detection, Procedia Computer Science, 122, 916-923.
Vatsa, M., Singh, R. ve Noore, A., 1997, Improving biometric recognition accuracy and robustness using DWT and SVM watermarking, p.
Vatsa, M., Singh, R. ve Noore, A., 2005, Improving biometric recognition accuracy and robustness using DWT and SVM watermarking, IEICE Electronic Express, 2, 362-367.
-
https://datavizcatalogue.com/TR/yontemleri/kutu_biyiki_grafikleri.html:
[23.07.2018].
Wang, G. G., Deb, S. ve Coelho, L. d. S., 2015, Elephant Herding Optimization, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), 1-5.
Wikimedia Commons contributors, 2016, File:23 salpchain frierson odfw (8253212250).jpg,
https://commons.wikimedia.org/w/index.php?title=File:23_salpchain_frierson_o
dfw_(8253212250).jpg&oldid=187809772: [06.05.2018].
Wikipedia contributors, 2018, "Corvus," Wikipedia, The Free Encyclopedia,
https://en.wikipedia.org/w/index.php?title=Corvus&oldid=846865293:
[15.04.2018].
Wolpert, D. ve Macready, W., 1997, Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1(1), 67-82, p. Wu, C.-H., Tzeng, G.-H. ve Lin, R.-H., 2009, A Novel hybrid genetic algorithm for kernel
function and parameter optimization in support vector regression, Expert Systems with Applications, 36 (3, Part 1), 4725-4735.
Xian, G.-m., 2010, An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM, Expert Systems with Applications, 37 (10), 6737-6741.
Yang, X.-S., 2009, Firefly Algorithms for Multimodal Optimization, Stochastic Algorithms: Foundations and Applications, Berlin, Heidelberg, 169-178.
Yang, X.-S. ve Deb, S., 2009, Cuckoo Search via Lévy flights, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210-214.
Yang, X.-S., 2010a, A New Metaheuristic Bat-Inspired Algorithm, In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Eds: González, J. R., Pelta, D. A., Cruz, C., Terrazas, G. ve Krasnogor, N., Berlin, Heidelberg: Springer Berlin Heidelberg, p. 65-74.
Yang, X.-S., 2010b, Firefly Algorithm, Stochastic Test Functions and Design Optimisation, p.
Yang, X.-S., 2010c, Nature-Inspired Metaheuristic Algorithms, p.
Yang, X.-S. ve Wiley, I., 2010, Engineering optimization : an introduction with metaheuristic applications.
Yang, X.-S., 2011, Metaheuristic Optimization, Scholarpedia, 6, 11472.
Yang, X.-S., Karamanoglu, M. ve He, X., 2014, Flower pollination algorithm: A novel approach for multiobjective optimization, Engineering Optimization, 46 (9), 1222-1237.
Yu, J. J. Q. ve Li, V. O. K., 2015, A social spider algorithm for global optimization, Applied Soft Computing, 30, 614-627.
Zhang, X., Chen, X. ve He, Z., 2010, An ACO-based algorithm for parameter optimization of support vector machines, Expert Systems with Applications, 37 (9), 6618-6628.
Zong Woo, G., Joong Hoon, K. ve Loganathan, G. V., 2001, A New Heuristic Optimization Algorithm: Harmony Search, SIMULATION, 76 (2), 60-68.
: Burak Tezcan : : / 1990 Telefon : (539) 512 5739 Faks : e-mail : btezcan@selcuk.edu.tr Derece Lise : 2008
Üniversite : Pamukkale Üniversitesi Mühendislik Fakültesi
, Merkez, Denizli 2014 Yüksek Lisans : Selçuk Üniversitesi Fen Bilimleri Enstitüsü
Bilgisayar Bilimleri Merkez, Konya
2018
Kurum Görevi
2016-.. Selçuk Üniversitesi Teknoloji Fak.
UZMANLIK ALANI
YAYINLAR
Tezcan B., Golcuk A., Tasdemir S., Balci M., Analysis Of A Metaheuristic Optimization Algorithm For Data Classification, International Conference, ICENTE, Page 25, Konya, Turkey, December 07-09, 2017.
Tezcan B., Tasdemir S., Golcuk A., Balci M., Optimizing Support Vector Machine
Parameters, 7th In -