Browsing by Author "Lin, Shan-Hui"

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Determining the optimal re-sampling strategy for a classification model with imbalanced data using design of experiments and response surface methodologies
(2011-04-01)
Tong, Lee-Ing
;
Chang, Yung-Chia
;
Lin, Shan-Hui
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Imbalanced data are common in many machine learning applications. In an imbalanced data set, the number of instances in at least one class is significantly higher or lower than that in other classes. Consequently, when classification models with imbalanced data are developed, most classifiers are subjected to an unequal number of instances in each class, thus failing to construct an effective model. Balancing sample sizes for various classes using a re-sampling strategy is a conventional means of enhancing the effectiveness of a classification model for imbalanced data. Despite numerous attempts to determine the appropriate re-sampling proportion in each class by using a trial-and-error method in order to construct a classification model with imbalanced data (Barandela, Vadovinos, Sanchez, & Ferri, 2004; He, Han, & Wang, 2005; Japkowicz, 2000; McCarthy, Zabar, & Weiss, 2005), the optimal strategy for each class may be infeasible when using such a method. Therefore, this work proposes a novel analytical procedure to determine the optimal re-sampling strategy based on design of experiments (DOE) and response surface methodologies (RSM). The proposed procedure, S-RSM, can be utilized by any classifier. Also, C4.5 algorithm is adopted for illustration. The classification results are evaluated by using the area under the receiver operating characteristic curve (AUC) as a performance measure. Among the several desirable features of the AUC index include independence of the decision threshold and invariance to a priori class probabilities. Furthermore, five real world data sets demonstrate that the higher AUC score of the classification model based on the training data obtained from the S-RSM is than that obtained using oversampling approach or undersampling approach. (C) 2010 Elsevier Ltd. All rights reserved.
Using Experimental Design to Determine the Re-Sampling Strategy for Developing a Classification Model for Imbalanced Data
(2009)
Tong, Lee-Ing
;
Chang, Yung-Chia
;
Lin, Shan-Hui
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Imbalanced data are often found in many real-world applications in machine learning. In an imbalanced data set, the number of instances in at least one class is significantly greater or smaller than that in other classes. Consequently, when developing a classification model with imbalanced data, most classifiers are subjected to the unequal number of instances in each class and thereby fail to construct an accurate classification model. Balance the sample sizes from different classes using re-sampling strategy is a common approach to enhance the accuracy of a classification model for an imbalanced data. Many studies utilized try-and-error method to determine the appropriate sampling proportion in each class for imbalanced data. The try-and-error method may not effectively classify the imbalanced data if the sampling strategy determined by the try-and-error method does not include the optimal sampling strategy. The conventional under-sampling strategy or over-sampling strategy determines just a specified sampling strategy. If the optimal sampling proportion for each class is not the specific sampling strategy determined by over-sampling approach or under-sampling approach, the classifiers cannot develop an effective classification model either. This study proposes a procedure to determine the optimal re-sampling strategy using design of experiments (D.O.E.). The proposed procedure can be utilized by any classifier. Finally, the classification model based on the training data obtained from the proposed procedure is verified to be more accurate than that obtained using the try-and-error method, over-sampling approach or under-sampling approach.