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1860797350199427072
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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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2024-08-26 23:06:18
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Restricted Document
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12364
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UniSZA
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[1] C. Pereira, L. Alexandre, A. M. Mendonc ̧a, and A. Campilho, A multiclassifier approach for lung nodule classification, LNCS., 4142 (2006), 612–623. [2] M. Makhtar, L. Yang, D. Neagu and M. J. Ridley, Optimisation of classifier ensemble for predictive toxicology application, in ‘Proceedings of the 14th International Conference on Modelling and Simulation (UKSim2012)’, IEEE, (2012), 1–6. [3] L. Rokach, Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography, Comput. Stat. Data Anal., 53 (2009), 4046–4072. [4] L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, WileyInterscience, (2005). [5] T. Woloszynski, and M. Kurzynski, , A probabilistic model of classifier competence for dynamic ensemble selection, Pattern Recogn., 44 (2011), 2656–2668. [6] T. G. Dietterich, Ensemble methods in machine learning, in J. Kittler & F. Roli, eds, ‘First International Workshop on Multiple Classifier Systems’, Lecture Notes in Computer Science, Springer - Verlag, New York, (2000), 1–15. [7] L. Breiman, Bagging Prediction. Machine Learning, 24 (1996), 123-140. [8] R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods". Annals of Statistics, 6 (1998), 1651- 1686. [9] D. Neagu, M. V. Craciun, S. A. Stroia, and S. Bumbaru, "Hybrid Intelligent Systems for Predictive Toxicology - a Distributed Approach", International Conference on Intelligent Systems Design and Applications, (2005), 26-31. [10] W. Wang, D. Partridge, and J. Etherington, "Hybrid ensembles and coincidentfailure diversity", Proc. IJCNN '01 International Joint Conference on In Neural Networks, 4 (2001), 2376-2381. [11] A. Kumaravel, "Comparison of two MultiClassification Approaches for Detecting Network Attacks", WASJ, 27 Issue 11 (2013), 1461-1465. [12] S. D. Bay, D. Kibler, M. J. Pazzani and P. Smyth, The uci kdd archive of large data sets for data mining research and experimentation, in ‘SIGKDD Explorations’, (2000), pp. 14–18. [13] I. Witten, E. Frank, and M. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann series in data management systems, Elsevier Science & Technology, (2011). [14] I. H. Witten, E. Frank, L. Trigg, M. Hall, G. Holmes and S. J. Cunningham, Weka: Practical machine learning tools and techniques with Java implementations, in ‘In Proceedings of ICONIP/ANZIIS/ANNES’99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems’, (1999), pp. 192–196. [15] L. Li, R. Stolkin, L. Jiao, F. Liu and S. Wang. A compressed sensing approach for efficient ensemble learning. Pattern Recognition, (2014). [16] A. Dal Pozzolo, O. Caelen, Y. A. Le Borgne, S. Waterschoot and G. Bontempi, Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41 Issues 10 (2014), 4915-4928. [17] N. S. Kamarudin, M. Makhtar, S. A. Fadzli, M. Mohamad, F. S. Mohamad, M. F. A. Kadir, Comparison Of Image Classification Techniques Using Caltech 101 Dataset, Journal of Theoretical and Applied Information Technology, 71 Issues 1 (2015), pp. 79-86.
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12364 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12364 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 2 1.6 Adobe Acrobat 20.6 2024-08-26 23:06:18 6664-01-FH02-FIK-15-03851.pdf UniSZA Private Access Optimizing Sensitivity and Specificity of Ensemble Classifiers for Diabetic Patients Journal of Theoretical and Applied Information Technology Predicting the right class for a certain disease in the medical-related field is very critical. The effects of misclassification of the class could be very risky because it may lead to the mistreatment of the patient. The most important classification performance measurements in medical fields are sensitivity, specificity and accuracy. This research aims to focus on the relationship between these three measurements. Misjudgements in classifying a person to a particular disease will prevent him/her from getting the correct treatment. Thus, the accuracy in classifying such medical data should be at the highest. Nevertheless, the most significant measurement is to have the highest sensitivity, because this will show that the classifier correctly classifies the patient who had a positive symptom of a particular disease. By using a single classifier, it is impossible to get the highest sensitivity. Thus, this paper proposed an ensemble method that aimed to increase the sensitivity as well as to improve the accuracy of the classification. The proposed method optimises the three performance measures by giving weights that composed of the proposed objective function. The results showed that the ensemble method is significant to achieve the highest accuracy of 76% with 84% sensitivity and 63% specificity for diabetic dataset from UCI medical data repositories. 81 1 Asian Research Publishing Network Asian Research Publishing Network 1-7 [1] C. Pereira, L. Alexandre, A. M. Mendonc ̧a, and A. Campilho, A multiclassifier approach for lung nodule classification, LNCS., 4142 (2006), 612–623. [2] M. Makhtar, L. Yang, D. Neagu and M. J. Ridley, Optimisation of classifier ensemble for predictive toxicology application, in ‘Proceedings of the 14th International Conference on Modelling and Simulation (UKSim2012)’, IEEE, (2012), 1–6. [3] L. Rokach, Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography, Comput. Stat. Data Anal., 53 (2009), 4046–4072. [4] L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms, WileyInterscience, (2005). [5] T. Woloszynski, and M. Kurzynski, , A probabilistic model of classifier competence for dynamic ensemble selection, Pattern Recogn., 44 (2011), 2656–2668. [6] T. G. Dietterich, Ensemble methods in machine learning, in J. Kittler & F. Roli, eds, ‘First International Workshop on Multiple Classifier Systems’, Lecture Notes in Computer Science, Springer - Verlag, New York, (2000), 1–15. [7] L. Breiman, Bagging Prediction. Machine Learning, 24 (1996), 123-140. [8] R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, "Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods". Annals of Statistics, 6 (1998), 1651- 1686. [9] D. Neagu, M. V. Craciun, S. A. Stroia, and S. Bumbaru, "Hybrid Intelligent Systems for Predictive Toxicology - a Distributed Approach", International Conference on Intelligent Systems Design and Applications, (2005), 26-31. [10] W. Wang, D. Partridge, and J. Etherington, "Hybrid ensembles and coincidentfailure diversity", Proc. IJCNN '01 International Joint Conference on In Neural Networks, 4 (2001), 2376-2381. [11] A. Kumaravel, "Comparison of two MultiClassification Approaches for Detecting Network Attacks", WASJ, 27 Issue 11 (2013), 1461-1465. [12] S. D. Bay, D. Kibler, M. J. Pazzani and P. Smyth, The uci kdd archive of large data sets for data mining research and experimentation, in ‘SIGKDD Explorations’, (2000), pp. 14–18. [13] I. Witten, E. Frank, and M. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann series in data management systems, Elsevier Science & Technology, (2011). [14] I. H. Witten, E. Frank, L. Trigg, M. Hall, G. Holmes and S. J. Cunningham, Weka: Practical machine learning tools and techniques with Java implementations, in ‘In Proceedings of ICONIP/ANZIIS/ANNES’99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems’, (1999), pp. 192–196. [15] L. Li, R. Stolkin, L. Jiao, F. Liu and S. Wang. A compressed sensing approach for efficient ensemble learning. Pattern Recognition, (2014). [16] A. Dal Pozzolo, O. Caelen, Y. A. Le Borgne, S. Waterschoot and G. Bontempi, Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41 Issues 10 (2014), 4915-4928. [17] N. S. Kamarudin, M. Makhtar, S. A. Fadzli, M. Mohamad, F. S. Mohamad, M. F. A. Kadir, Comparison Of Image Classification Techniques Using Caltech 101 Dataset, Journal of Theoretical and Applied Information Technology, 71 Issues 1 (2015), pp. 79-86.
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| spellingShingle |
Optimizing Sensitivity and Specificity of Ensemble Classifiers for Diabetic Patients
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| summary |
Predicting the right class for a certain disease in the medical-related field is very critical. The effects of misclassification of the class could be very risky because it may lead to the mistreatment of the patient. The most important classification performance measurements in medical fields are sensitivity, specificity and accuracy. This research aims to focus on the relationship between these three measurements. Misjudgements in classifying a person to a particular disease will prevent him/her from getting the correct treatment. Thus, the accuracy in classifying such medical data should be at the highest. Nevertheless, the most significant measurement is to have the highest sensitivity, because this will show that the classifier correctly classifies the patient who had a positive symptom of a particular disease. By using a single classifier, it is impossible to get the highest sensitivity. Thus, this paper proposed an ensemble method that aimed to increase the sensitivity as well as to improve the accuracy of the classification. The proposed method optimises the three performance measures by giving weights that composed of the proposed objective function. The results showed that the ensemble method is significant to achieve the highest accuracy of 76% with 84% sensitivity and 63% specificity for diabetic dataset from UCI medical data repositories.
|
| title |
Optimizing Sensitivity and Specificity of Ensemble Classifiers for Diabetic Patients
|
| title_full |
Optimizing Sensitivity and Specificity of Ensemble Classifiers for Diabetic Patients
|
| title_fullStr |
Optimizing Sensitivity and Specificity of Ensemble Classifiers for Diabetic Patients
|
| title_full_unstemmed |
Optimizing Sensitivity and Specificity of Ensemble Classifiers for Diabetic Patients
|
| title_short |
Optimizing Sensitivity and Specificity of Ensemble Classifiers for Diabetic Patients
|
| title_sort |
optimizing sensitivity and specificity of ensemble classifiers for diabetic patients
|