Makine Öğrenmesini Kullanarak Açılabilirliğe Dayalı Yüzey Noktalarını Sınıflandırma

Öz

Sınıflandırıcılar K-en yakın komşu (KNN), Çok sınıflı destek vektör makinesi (MSVM), Karar Ağacı (DT), Ayrım Analizi (DA), Naive Bayes (NB), Rastgele Orman (RF) ve Topluluk Ağacı (ET) makine öğrenmesinde en iyi bilinen yöntemlerdir ve örüntü tanıma, tıbbi hastalık analizi, kullanıcı akıllı telefon sınıflandırması, metin sınıflandırması gibi birçok alanda kullanılmaktadır. Bu makale, makine öğrenmesinde en bilinen sınıflandırma yöntemlerini ve asal eğrilikleri, binormal vektörü, normal vektör ve binormal vektörler arasındaki açının kosinüs değerini kullanarak 3B yüzey noktası tipi sınıflandırması için yeni bir çerçeve sunmaktadır. Bu çalışmanın amacı, veri noktalarını açılabilirliklerine göre sınıflandırmaktır. Ayrıca, bu yöntemler arasındaki karşılaştırma, çeşitli 3B yüzey örneği kullanılarak doğruluk ve işleme süresine dayalı olarak açılabilirliği ölçmek için verilmiştir.

Anahtar Kelimeler

• Makine öğrenmesi
• sınıflandırma
• asal eğrilikler
• binormal vektör.

Classifying Surface Points Based on Developability Using Machine Learning

Abstract

The classifiers K-nearest neighbor (KNN), Multiclass support vector machine (MSVM), Decision Tree (DT), Discriminate Analysis (DA), Naive Bayes (NB), Random Forest (RF), and Ensemble Tree (ET) are the most well-known methods in machine learning. They are used in many fields like pattern recognition, medical disease analysis, user smartphone classification, text classification, etc. This paper presents a new framework for 3D surface point type classification using the most known classification methods in machine learning and the principal curvatures, the binormal vector, the cosine value of the angle between the normal vector and binormal vectors. The purpose of this study is to classify data points according to their developability. Also, the comparison between these methods is given to measure developability based on the accuracy and the processing time using several 3D surface examples.

Keywords

• Machine learning
• classification
• principal curvatures
• binormal vector.

Kaynakça

1. Krsek, P., Lukacs, G. and Martin, R. R. (1998). Algorithms for computing curvatures from range data. In In The Mathematics of Surfaces VIII, Information Geometers, pages 1–16.
2. Vemuri, B. C., Mitiche, A. and Aggarwal, J. K. (1986). Curvature based representation of objects from range data. Image and Vision Computing, 4:107–14.
3. Deng, H., Zhang, W., Mortensen, E., Dietterich, T. and Shapiro, L. (2007) Principal curvature-based region detector for object recognition. In 2007 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8.
4. Tremblay-Darveau, C., Sheeran, P. S., Vu, C. K., Williams, R., Bruce, M. and Burns, P. N. (2018). 3-d perfusion imaging using principal curvature detection rendering. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 65(12):2286–2295.
5. Wang, F., You, H.J., Qiu, X.L. and Yao, X.H. (2018). Shape similarity measure method based on principal curvature enhancement distance transformation. Journal of Infrared and Millimeter Waves, 37(1).
6. Rimkus, K., Lipnickas, A., & Sinkevicius, S. (2014). Classification of 3D Point Cloud Using Numerical Surface Signatures on Interest Points. Elektronika Ir Elektrotechnika, 20, 8-11.
7. Shen, L., Ford, J.C., Makedon, F., & Saykin, A.J. (2004). A surface-based approach for classification of 3D neuroanatomic structures. Intell. Data Anal., 8, 519-542.
8. Zheng, H., Fang, L., Ji, M., Strese, M., Özer, Y. and Steinbach, E. (2016). "Deep Learning for Surface Material Classification Using Haptic and Visual Information," in IEEE Transactions on Multimedia, vol. 18, no. 12, pp. 2407-2416.

9. Pressley, A. (2010). Elementary Differential Geometry. Springer-Verlag London.
10. Kreyszig, E. (1991). Differential Geometry. Dover Publications, Inc., New York.
11. Patil, S. and Kulkarni, U. (2019). Accuracy Prediction for Distributed Decision Tree using Machine Learning approach, in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1365–1371.
12. Duda, R. O., Hart, P. E. and Stork, D. G. (2001). Pattern Classification, 2nd edition. John Wiley & Sons, New York.
13. Sulaiman, M. A. (2020). Evaluating Data Mining Classification Methods Performance in Internet of Things Applications, Journal of Soft Computing and Data Mining, vol. 1, no. 2, pp. 11–25.
14. Ahmed, O. and Brifcani, A. (2019). Gene Expression Classification Based on Deep Learning, in 2019 4th Scientific International Conference Najaf (SICN), Al-Najef, Iraq, pp. 145–149.
15. Patil, D. V. and Bichkar, R. S. (2006). A Hybrid Evolutionary Approach To Construct Optimal Decision Trees With Large Data Sets, in 2006 IEEE International Conference on Industrial Technology, pp. 429–433.
16. Priyanka and Kumar, D. (2020). Decision tree classifier: a detailed survey,” International Journal of Information and Decision Sciences, vol. 12, no. 3, pp. 246–269.
17. Ahuja, Y., & Yadav, S.K. (2012). Multiclass Classification and Support Vector Machine By Yashima Ahuja & Yadav.
18. Tharwat, Alaa. (2016). Linear vs. quadratic discriminant analysis classifier: a tutorial. International Journal of Applied Pattern Recognition. 3. 145. 10.1504/IJAPR.2016.079050.
19. Berrar, Daniel. (2018). Bayes’ Theorem and Naive Bayes Classifier. 10.1016/B978-0-12-809633-8.20473-1.
20. Breiman, L. (2001). Random Forests. Machine Learning 45, 5–32.
21. Dietterich T.G. (2000). Ensemble Methods in Machine Learning. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg.