Makine Öğrenmesini Kullanarak Açılabilirliğe Dayalı Yüzey Noktalarını Sınıflandırma

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

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.,

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