Browsing by Author "Huang, Cheng-Lung"

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Credit scoring with a data mining approach based on support vector machines
(2007-11-01)
Huang, Cheng-Lung
;
Chen, Mu-Chen
;
Wang, Chieh-Jen
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
The credit card industry has been growing rapidly recently, and thus huge numbers of consumers' credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer's credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant's credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant's credit score from the applicant's input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods. (c) 2006 Elsevier Ltd. All rights reserved.
Incorporating frequency, recency and profit in sequential pattern based recommender systems
(2013)
Huang, Cheng-Lung
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Chen, Mu-Chen
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Huang, Wen-Chen
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Huang, Sheng-Huang
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運輸與物流管理系註：原交通所+運管所
Customers usually change their purchase interests in the short product life cycle of the e-commerce environment. Therefore, recent transaction patterns should have a greater effect on the customer preferences. From the seller's point of view, an e-commerce recommender system should focus on the profit of recommendation. This study proposes a new sequential pattern mining algorithm that incorporates the concepts of frequency, recency, and profit to discover frequent, recent, and profitable sequential patterns, called FRP-sequences. Based on the discovered sequential patterns, this study develops a collaborative recommender system to improve recommendation accuracy for customers and the profit of recommendation from the seller's perspective. The proposed recommender system clusters customers, discovers FRP-sequences for each cluster, and then recommends items to the target customers based on their frequent, recent, and profitable FRP-sequences. In the stage of discovering FRP-sequences, the transaction patterns near the current time period and profitable items are weighted more heavily to improve profit. This study uses a public food mart database to determine the performance of the proposed approach, and compares it with traditional recommendation models. The proposed system performs better than traditional recommendation models in both recommendation accuracy and profit.
Predicting breast tumor via mining DNA viruses with decision tree
(2006)
Chen, Mu-Chen
;
Liao, Hung-Chang
;
Huang, Cheng-Lung
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Breast cancer is a serious problem, especially the young women in Taiwan. Until now, in the most medical researches, the reasons for suffering from breast tumor are unclear. However, most medical researches proved that DNA viruses are the high-risk factors closely related to human cancers. In recent years, hospitals and health organizations have been furnished with modern computerized medical equipment for data collection, monitoring and diagnosis. Additionally, these data are stored in large medical information systems for analysis purpose. Developing truthful and reliable classifiers for diagnosis and prognosis has become an essential task in medical and healthcare. It was reported with increasing confirmation that the machine learning algorithms can generate more accurate and transparent classifiers and decision rules for physicians than traditional methodologies. In the machine learning algorithms, decision trees have been already successfully used in the areas of medicine and healthcare. In this paper, an algorithm of decision trees, Chi-squared Automatic Interaction Detection (CHAID), is applied to build a classifier for predicting breast cancer and fibroadenoma. The results demonstrate that the decision tree technique is more favorably than logistic regression in terms of rule accuracy and knowledge transparency to physicians. Furthermore, the medical classifier can assist inexperienced physicians to prevent from misdiagnosis.
Prediction model building and feature selection with support vector machines in breast cancer diagnosis
(2008-01-01)
Huang, Cheng-Lung
;
Liao, Hung-Chang
;
Chen, Mu-Chen
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Breast cancer is a serious problem for the young women of Taiwan. Some medical researches have proved that DNA viruses are one of the high-risk factors closely related to human cancers. Five DNA viruses are studied in this research: specific types of HSV-1 (herpes simplex virus type 1), EBV (Epstein-Barr virus), CMV (cytomegalovirus), HPV (human. papillomavirus), and HHV-8 (human herpesvirus-8). The purposes of this study are to obtain the bioinformatics about breast tumor and DNA viruses, and to build an accurate diagnosis model about breast cancer and fibroadenoma. Research efforts have reported with increasing confirmation that the support vector machine (SVM) has a greater accurate diagnosis ability. Therefore, this study constructs a hybrid SVM-based strategy with feature selection to render a diagnosis between the breast cancer and fibroadenoma and to find the important risk factor for breast cancer. The results show that JHSV-l, HHV-8} or {HSV-1, HHV-8, CMV) are the most important features and that the diagnosis model achieved high classification accuracy, at 86% of average overall hit rate. A Linear discriminate analysis (LDA) diagnosis model is also constructed in this study. The LDA model shows that {HSV-1, HHV-8, EBV} or {HSV-1, HHV-8} are significant factors which are similar to that of the SVM-based classifier. However, the classificatory accuracy of the SVM-based classifier is slightly better than that of LDA in the negative hit ratio, positive hit ratio, and overall hit ratio. (c) 2006 Elsevier Ltd. All rights reserved.