Tuning Suitable Features Selection using Mixed Waste Classification Accuracy
- 10.2991/jrnal.k.211108.014How to use a DOI?
- Features reduction; features optimization; higher classification rate; mixed waste classification
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.
- © 2021 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Hassan Mehmood Khan AU - Norrima Mokhtar AU - Heshalini Rajagopal AU - Anis Salwa Mohd Khairuddin AU - Wan Amirul Bin Wan Mohd Mahiyidin AU - Noraisyah Mohamed Shah AU - Raveendran Paramesran PY - 2021 DA - 2021/12/29 TI - Tuning Suitable Features Selection using Mixed Waste Classification Accuracy JO - Journal of Robotics, Networking and Artificial Life SP - 298 EP - 303 VL - 8 IS - 4 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.211108.014 DO - 10.2991/jrnal.k.211108.014 ID - Khan2021 ER -