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1860797481764257792
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2016-04-13 10:00:16
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Restricted Document
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12909
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UniSZA
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[1] A. M. S. El-bohy, A. I. Hashad, H. S. Taha. 2015. Performance Evaluation of Hepatitis Diagnosis using Single and Multi-Classifiers Fusion. 4(4):293–298. [2] A. Rahman, S. Tasnim. 2014. Ensemble Classifiers and Their Applications : A Review. 10(1): 31–35. [3] B. Zeng, Z. Luo, J. Wei. 2008. Sea Water Pollution Assessment Based On Ensemble of Classifiers. pp. 241–245. [4] A. Chandrasekhar. 2014. Water quality is as important for ecosystems as for people; http://www.teebweb.org/water-quality-is-asimportant-for-ecosystems-as-for-people/. [5] S. Y. Muhammad, M. Makhtar, A. Rozaimee, A. Abdul, A. A. Jamal. 2015. Classification Model for Water Quality using Machine Learning Techniques. Int. J. Softw. Eng. Its Appl. 9(6): 45–52. [6] R. Rosly, M. Makhtar, M. K. Awang, and M. A. Nordin. 2015. The Study on the Accuracy of Classifiers for Water Quality Application. Int. J. u- eServ. Sci. Technol. 8(3): 145–154. [7] G. I. Salama, M. B. Abdelhalim, M. A. Zeid. 2012. Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers. Int. J. Comput. Inf. Technol. (2277 – 0764). 1(1). [8] Q. Chen and A. E. Mynett. 2004. Predicting Phaeocystis globosa bloom in Dutch coastal waters by decision trees and nonlinear piecewise regression. Ecol. Modell. 176(3–4): 277–290. [9] J. Camejo and O. Pacheco. 2013. Classifier for Drinking Water Quality in Real Time. pp. 1–4. [10] S. Areerachakul, S. Sanguansintukul. 2010. Classification and Regression Trees and MLP Neural Network to Classify Water Quality of Canals in Bangkok, Thailand. 1(1): 43–50. [11] A. Naumoski, K. Mitreski. 2010. Naïve Bayes technique for diatoms classification with discretised input. pp. 21–30. [12] W. Huang, F. Huang, J. Song. 2010. An SVM model for Water Quality Monitoring Using Remote Sensing Image. 1(4): 186–189. [13] M. Makhtar, Y. Longzhi, D. Neagu, M. Ridley. 2012 Optimisation of Classifier Ensemble for Predictive Toxicology Applications. Computer Modelling and Simulation (UKSim); http://www.academia.edu/ 4750498/Optimisation_of_Classifier_Ensemble_for_P redictive_Toxicology_Applications.
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norman
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12909 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12909 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 1424 53 53 772 1424x772 2016-04-13 10:00:16 7216-01-FH02-FIK-16-05680.jpg UniSZA Private Access Multi-classifier models to improve accuracy of water quality application ARPN Journal of Engineering and Applied Sciences This paper presents a comparison among the different classifiers such as Naïve Bayes (NB), decision tree (J48), Sequential Minimal Optimization (SMO), Multi-Layer Perception (MLP), and Instance Based for K-Nearest neighbor (IBK) on water quality for datasets of Kinta River, Perak, Malaysia. Classification accuracy and confusion matrix were used in this research based on a 10-fold cross validation method. Then, a fusion at classification level between these classifiers was applied to get the highest accuracy and see which the most suitable multi-classifier approach for the datasets. The water quality datasets were taken from the East Coast Environmental Research Institute (ESERI) of University of Sultan Zainal Abidin (UniSZA). The water quality classes were evaluated using 10 factor indices, namely DO Sat, DO Mgl, BOD Mgl, COD Mgl, TS Mgl, DO Index, AN Index, SS Index, Class, and Degree of Pollution. The results showed that the classification using fusion between IBK+MLP, IBK+SMO, and IBK+MLP+NB+SMO was superior to the other classifiers that achieved the higher accuracy with the same percentage of 93.98%. Thus, using multiclassifier approaches can achieve better accuracy than the single ones. 11 5 3208-3211 [1] A. M. S. El-bohy, A. I. Hashad, H. S. Taha. 2015. Performance Evaluation of Hepatitis Diagnosis using Single and Multi-Classifiers Fusion. 4(4):293–298. [2] A. Rahman, S. Tasnim. 2014. Ensemble Classifiers and Their Applications : A Review. 10(1): 31–35. [3] B. Zeng, Z. Luo, J. Wei. 2008. Sea Water Pollution Assessment Based On Ensemble of Classifiers. pp. 241–245. [4] A. Chandrasekhar. 2014. Water quality is as important for ecosystems as for people; http://www.teebweb.org/water-quality-is-asimportant-for-ecosystems-as-for-people/. [5] S. Y. Muhammad, M. Makhtar, A. Rozaimee, A. Abdul, A. A. Jamal. 2015. Classification Model for Water Quality using Machine Learning Techniques. Int. J. Softw. Eng. Its Appl. 9(6): 45–52. [6] R. Rosly, M. Makhtar, M. K. Awang, and M. A. Nordin. 2015. The Study on the Accuracy of Classifiers for Water Quality Application. Int. J. u- eServ. Sci. Technol. 8(3): 145–154. [7] G. I. Salama, M. B. Abdelhalim, M. A. Zeid. 2012. Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers. Int. J. Comput. Inf. Technol. (2277 – 0764). 1(1). [8] Q. Chen and A. E. Mynett. 2004. Predicting Phaeocystis globosa bloom in Dutch coastal waters by decision trees and nonlinear piecewise regression. Ecol. Modell. 176(3–4): 277–290. [9] J. Camejo and O. Pacheco. 2013. Classifier for Drinking Water Quality in Real Time. pp. 1–4. [10] S. Areerachakul, S. Sanguansintukul. 2010. Classification and Regression Trees and MLP Neural Network to Classify Water Quality of Canals in Bangkok, Thailand. 1(1): 43–50. [11] A. Naumoski, K. Mitreski. 2010. Naïve Bayes technique for diatoms classification with discretised input. pp. 21–30. [12] W. Huang, F. Huang, J. Song. 2010. An SVM model for Water Quality Monitoring Using Remote Sensing Image. 1(4): 186–189. [13] M. Makhtar, Y. Longzhi, D. Neagu, M. Ridley. 2012 Optimisation of Classifier Ensemble for Predictive Toxicology Applications. Computer Modelling and Simulation (UKSim); http://www.academia.edu/ 4750498/Optimisation_of_Classifier_Ensemble_for_P redictive_Toxicology_Applications.
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| spellingShingle |
Multi-classifier models to improve accuracy of water quality application
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| summary |
This paper presents a comparison among the different classifiers such as Naïve Bayes (NB), decision tree (J48), Sequential Minimal Optimization (SMO), Multi-Layer Perception (MLP), and Instance Based for K-Nearest neighbor (IBK) on water quality for datasets of Kinta River, Perak, Malaysia. Classification accuracy and confusion matrix were used in this research based on a 10-fold cross validation method. Then, a fusion at classification level between these classifiers was applied to get the highest accuracy and see which the most suitable multi-classifier approach for the datasets. The water quality datasets were taken from the East Coast Environmental Research Institute (ESERI) of University of Sultan Zainal Abidin (UniSZA). The water quality classes were evaluated using 10 factor indices, namely DO Sat, DO Mgl, BOD Mgl, COD Mgl, TS Mgl, DO Index, AN Index, SS Index, Class, and Degree of Pollution. The results showed that the classification using fusion between IBK+MLP, IBK+SMO, and IBK+MLP+NB+SMO was superior to the other classifiers that achieved the higher accuracy with the same percentage of 93.98%. Thus, using multiclassifier approaches can achieve better accuracy than the single ones.
|
| title |
Multi-classifier models to improve accuracy of water quality application
|
| title_full |
Multi-classifier models to improve accuracy of water quality application
|
| title_fullStr |
Multi-classifier models to improve accuracy of water quality application
|
| title_full_unstemmed |
Multi-classifier models to improve accuracy of water quality application
|
| title_short |
Multi-classifier models to improve accuracy of water quality application
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| title_sort |
multi-classifier models to improve accuracy of water quality application
|