An improvement algoithm for Iris classification by using Linear Support Vector Machine (LSVM), k-Nearest Neighbours (k-NN) and Random Nearest Neighbous (RNN) / Ahmad Haadzal Kamarulzalis and Mohd Asrul Affendi Abdullah
In machine learning, there are three type of learning branch that can used in classification procedures for data mining. Those branchconsist of supervised learning, unsupervised learning and reinforcement learning. This study focuses on supervised learning that seek to classif...
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| Main Authors: | , |
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| Format: | Article |
| Language: | en |
| Published: |
Unit Penerbitan UiTM Kelantan
2019
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/29220/1/39 https://ir.uitm.edu.my/id/eprint/29220/ https://jmcs.com.my/ |
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| Summary: | In machine learning, there are three type of learning branch that can used in classification procedures for data mining. Those branchconsist of supervised learning, unsupervised learning and reinforcement learning. This study focuses on supervised learning that seek to classify all the Iris dataset respect to three species (setosa, versicolor and virginica) in order them to mimic the actual dataset by using Linear Support Vector Machine (LSVM) , k-Nearest Neighbours (kNN) and Random Nearest Neighbours (RNN) as a method. Aims of this study is to improve an existing algorithm technique for classification. The ideas come from a combination of k-NN algorithm and ensemble concept. Next, is to identify the best model for classification procedures. Existing Performance Measurement Tools such as overall accuracy and misclassification error rate (MER) areused for each classifier. Random Nearest Neighbours (RNN) has the highest accuracy value with98% and2% misclassification error rate (MER) compare to other classifier. Therefore, Random Nearest Neighbors (RNN) is preferable for supervised learning classification procedures. |
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