Data Classification Using Combination of Five Machine Learning Techniques

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

doi:10.4236/jcc.2021.912004

Md Habibur Rahman, Abu Sayed Md Mostafizur Rahaman + Show 2 more

Open Access

https://doi.org/10.4236/jcc.2021.912004

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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.

Highlights

In this paper five widely used methods: Fuzzy weighted rule, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) are integrated in classification of Iris data
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)
In [1] authors use Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Fuzzy Inference System (FIS) for professional blogger classification, where FIS provides better results compared to Classification Based on Associations (CBA)

Summary

Introduction

In this paper five widely used methods: Fuzzy weighted rule, FIS, FCM, SVM and ANN are integrated in classification of Iris data. In [3], fuzzy weighted rules are used to classify Iris data using seven membership function (MFs). The fuzzy rule-based classification is found in [4] for classification of coronary artery disease data, where trapezoidal membership functions are used for input variables. We applied fuzzy c-mean clustering in Iris data classification; the similar concept is available in MR brain image segmentation in [5]. The entire algorithm of C-mean clustering is shown and the performance of image classification is compared with seven different methods and fuzzy c-mean clustering provides moderate result. Similar concept is found in [10] for breast cancer diagnosis, where three different types of kernels are used and accuracy is found above 90% for all cases

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