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Saini, R., Kumar, P., Roy, P.P. and Dogra, D.P. (2017) An Efficient Approach for Trajectory Classification Using FCM and SVM. 2017 IEEE Region 10 Symposium (TENSYMP), Cochin, 14-16 July 2017, 1-4.
https://doi.org/10.1109/TENCONSpring.2017.8070076

has been cited by the following article:

• TITLE: Data Classification Using Combination of Five Machine Learning Techniques

  AUTHORS: Md. Habibur Rahman, Jesmin Akhter, Abu Sayed Md. Mostafizur Rahaman, Md. Imdadul Islam

  KEYWORDS: Co-Variance of Fuzzy Rule, Objective Function, Surface Plot, Confusion Matrix, Scatterplot and Accuracy of Detection

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.12, December 30, 2021

  ABSTRACT: Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.