Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants
Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke and outdoor pollutants. This type of pollutants could emit dangerous gases such as carbon monoxide (CO), carbon dioxide (CO2), ozone (O3) and particulat...
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| Online Access: | http://eprints.utm.my/id/eprint/80703/ http://mitec.unikl.edu.my/wp-content/uploads/2018/08/13.-ANALYSIS-OF-FEATURE-SELECTION-WITH-K-NEAREST-NEIGHBOUR-KNN-TO-CLASSIFY-INDOOR-AIR-POLLUTANTS-1.pdf |
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my.utm.807032019-06-27T06:15:36Z http://eprints.utm.my/id/eprint/80703/ Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants Saad, S. M. Shakaff, A. Y. M. Hussein, M. Mohamad, M. Dzahir, M. A. M. Ahmad, Z. TJ Mechanical engineering and machinery Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke and outdoor pollutants. This type of pollutants could emit dangerous gases such as carbon monoxide (CO), carbon dioxide (CO2), ozone (O3) and particulate matter. These gases are usually safe for us to breathe in if they are emitted in safe quantity but if the amount of these gases exceeded the safe level, they might be hazardous to human being especially children and people with asthmatic problem. Therefore, a smart indoor air quality monitoring system (IAQMS) is needed that able to tell the occupants about which pollutant that trigger the indoor air pollution. In this study, an IAQMS that able to classify the air pollutants has been developed. This IAQMS applies a classification method based on K-Nearest Neighbour (KNN). It is used to classify the air pollutants based on five conditions: ambient air, human activity, presence of chemical products, presence of food and beverage and presence of fragrance. In order to get good and best classification accuracy, an analysis of several feature selection based on data pre-processing method is done to discriminate among of sources. The output from each data pre-processing method has been used as the input for the classification. The result shows that KNN analysis with the data pre-processing method for most of the features obtained remarkably high classification accuracy of above 97% and able to classify the air pollutants at high classification rate. UniKL MITEC 2017 Article PeerReviewed Saad, S. M. and Shakaff, A. Y. M. and Hussein, M. and Mohamad, M. and Dzahir, M. A. M. and Ahmad, Z. (2017) Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants. Malaysian Journal of Industrial Technology, 2 (2). pp. 1-5. ISSN 2462-2540 http://mitec.unikl.edu.my/wp-content/uploads/2018/08/13.-ANALYSIS-OF-FEATURE-SELECTION-WITH-K-NEAREST-NEIGHBOUR-KNN-TO-CLASSIFY-INDOOR-AIR-POLLUTANTS-1.pdf |
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TJ Mechanical engineering and machinery Saad, S. M. Shakaff, A. Y. M. Hussein, M. Mohamad, M. Dzahir, M. A. M. Ahmad, Z. Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants |
| description |
Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke and outdoor pollutants. This type of pollutants could emit dangerous gases such as carbon monoxide (CO), carbon dioxide (CO2), ozone (O3) and particulate matter. These gases are usually safe for us to breathe in if they are emitted in safe quantity but if the amount of these gases exceeded the safe level, they might be hazardous to human being especially children and people with asthmatic problem. Therefore, a smart indoor air quality monitoring system (IAQMS) is needed that able to tell the occupants about which pollutant that trigger the indoor air pollution. In this study, an IAQMS that able to classify the air pollutants has been developed. This IAQMS applies a classification method based on K-Nearest Neighbour (KNN). It is used to classify the air pollutants based on five conditions: ambient air, human activity, presence of chemical products, presence of food and beverage and presence of fragrance. In order to get good and best classification accuracy, an analysis of several feature selection based on data pre-processing method is done to discriminate among of sources. The output from each data pre-processing method has been used as the input for the classification. The result shows that KNN analysis with the data pre-processing method for most of the features obtained remarkably high classification accuracy of above 97% and able to classify the air pollutants at high classification rate. |
| format |
Article |
| author |
Saad, S. M. Shakaff, A. Y. M. Hussein, M. Mohamad, M. Dzahir, M. A. M. Ahmad, Z. |
| author_facet |
Saad, S. M. Shakaff, A. Y. M. Hussein, M. Mohamad, M. Dzahir, M. A. M. Ahmad, Z. |
| author_sort |
Saad, S. M. |
| title |
Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants |
| title_short |
Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants |
| title_full |
Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants |
| title_fullStr |
Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants |
| title_full_unstemmed |
Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants |
| title_sort |
analysis of feature selection with k-nearest neighbour (knn) to classify indoor air pollutants |
| publisher |
UniKL MITEC |
| publishDate |
2017 |
| url |
http://eprints.utm.my/id/eprint/80703/ http://mitec.unikl.edu.my/wp-content/uploads/2018/08/13.-ANALYSIS-OF-FEATURE-SELECTION-WITH-K-NEAREST-NEIGHBOUR-KNN-TO-CLASSIFY-INDOOR-AIR-POLLUTANTS-1.pdf |
| _version_ |
1643658491305394176 |
| score |
13.211869 |