Tuning suitable features selection using mixed waste classification accuracy

Hassan Mehmood Khan, Norrima Mokhtar, Heshalini Rajagopal, Anis Salwa Mohd Khairuddin, Wan Amirul Bin Wan Mohd Mahiyidin, Noraisyah Mohamed Shah, Raveendran Paramesran

Research output: Contribution to journalArticleResearchpeer-review


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.

Original languageEnglish
Pages (from-to)298-303
Number of pages6
JournalJournal of Robotics, Networking and Artificial Life
Issue number4
Publication statusPublished - Mar 2022
Externally publishedYes


  • features optimization
  • Features reduction
  • higher classification rate
  • mixed waste classification

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