Tuning suitable features selection using mixed waste classification accuracy
Classification accuracy can be used as method to tune suitable features. Some features can be mistakenly selected hence derailed the classification accuracy. Currently, feature optimization has gained many interests among researchers. Hence, this paper aims to demonstrate the effects of features red...
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2022
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my.um.eprints.432142023-11-21T03:25:56Z http://eprints.um.edu.my/43214/ Tuning suitable features selection using mixed waste classification accuracy Khan, Hassan Mehmood Mokhtar, Norrima Rajagopal, Heshalini Mohd Khairuddin, Anis Salwa Wan Mohd Mahiyidin, Wan Amirul Mohamed Shah, Noraisyah Paramesran, Raveendran TK Electrical engineering. Electronics Nuclear engineering Classification accuracy can be used as method to tune suitable features. Some features can be mistakenly selected hence derailed the classification accuracy. Currently, feature optimization has gained many interests among researchers. Hence, this paper aims to demonstrate the effects of features reduction and optimization for higher classification results of mixed waste. The most relevant features with respect to mix waste characteristic were observed with respect to classification accuracy. There are four stages of features selection. The first stage, 40 features were selected with training accuracy 79.59. Then, for second stage, better accuracy was obtained when redundant features were removed which accounted for 20 features with training accuracy of 81.42. As for the third stage 17 features were maintained at 90.69 training accuracy. Finally, for the fourth stage, additional two more features were removed, however the classification accuracy was decreased to less than 80. The experiments results showed that by observing the classification rate, certain features gave higher accuracy, while the others were redundant. Therefore, in this study, suitable features gave higher accuracy, on contrary, as the number of features increased, the accuracy rate were not necessarily higher. © 2022 The Authors. Atlantis Press 2022-03 Article PeerReviewed Khan, Hassan Mehmood and Mokhtar, Norrima and Rajagopal, Heshalini and Mohd Khairuddin, Anis Salwa and Wan Mohd Mahiyidin, Wan Amirul and Mohamed Shah, Noraisyah and Paramesran, Raveendran (2022) Tuning suitable features selection using mixed waste classification accuracy. Journal of Robotics, Networking and Artificial Life, 8 (4). 298 -303. ISSN 2352-6386, DOI https://doi.org/10.2991/jrnal.k.211108.014 <https://doi.org/10.2991/jrnal.k.211108.014>. 10.2991/jrnal.k.211108.014 |
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TK Electrical engineering. Electronics Nuclear engineering Khan, Hassan Mehmood Mokhtar, Norrima Rajagopal, Heshalini Mohd Khairuddin, Anis Salwa Wan Mohd Mahiyidin, Wan Amirul Mohamed Shah, Noraisyah Paramesran, Raveendran Tuning suitable features selection using mixed waste classification accuracy |
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Classification accuracy can be used as method to tune suitable features. Some features can be mistakenly selected hence derailed the classification accuracy. Currently, feature optimization has gained many interests among researchers. Hence, this paper aims to demonstrate the effects of features reduction and optimization for higher classification results of mixed waste. The most relevant features with respect to mix waste characteristic were observed with respect to classification accuracy. There are four stages of features selection. The first stage, 40 features were selected with training accuracy 79.59. Then, for second stage, better accuracy was obtained when redundant features were removed which accounted for 20 features with training accuracy of 81.42. As for the third stage 17 features were maintained at 90.69 training accuracy. Finally, for the fourth stage, additional two more features were removed, however the classification accuracy was decreased to less than 80. The experiments results showed that by observing the classification rate, certain features gave higher accuracy, while the others were redundant. Therefore, in this study, suitable features gave higher accuracy, on contrary, as the number of features increased, the accuracy rate were not necessarily higher. © 2022 The Authors. |
| format |
Article |
| author |
Khan, Hassan Mehmood Mokhtar, Norrima Rajagopal, Heshalini Mohd Khairuddin, Anis Salwa Wan Mohd Mahiyidin, Wan Amirul Mohamed Shah, Noraisyah Paramesran, Raveendran |
| author_facet |
Khan, Hassan Mehmood Mokhtar, Norrima Rajagopal, Heshalini Mohd Khairuddin, Anis Salwa Wan Mohd Mahiyidin, Wan Amirul Mohamed Shah, Noraisyah Paramesran, Raveendran |
| author_sort |
Khan, Hassan Mehmood |
| title |
Tuning suitable features selection using mixed waste classification accuracy |
| title_short |
Tuning suitable features selection using mixed waste classification accuracy |
| title_full |
Tuning suitable features selection using mixed waste classification accuracy |
| title_fullStr |
Tuning suitable features selection using mixed waste classification accuracy |
| title_full_unstemmed |
Tuning suitable features selection using mixed waste classification accuracy |
| title_sort |
tuning suitable features selection using mixed waste classification accuracy |
| publisher |
Atlantis Press |
| publishDate |
2022 |
| url |
http://eprints.um.edu.my/43214/ |
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1783876741493686272 |
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13.211869 |