Nan-Hsing Chiu

Ching Yun University, Taoyuan City, Taiwan, Taiwan

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Publications (15)6.77 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Web forums become the means of online communication and information sharing sources for the learning about health care and related treatment knowledge. By adopting web crawlers and natural language processing techniques, the automatic identification approach of the concerned HIV-related messages is proposed to facilitate the health authorities and social support groups in instant counseling. The proposed supervised GA/k-means for classification approach can help construct an effective identification and classification model with acceptable classification performance accompanied with its full flexibility to develop different fitness functions in accordance with the need of different requirements. Furthermore, with the aid of correspondence analysis, the most frequently used terms in concerned HIV-related messages are identified and focus on risky sexual behavior whereas unconcerned messages are those who of worried well.
    AIDS Care 07/2013; · 1.60 Impact Factor
  • Nan-Hsing Chiu
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    ABSTRACT: Accurately predicting fault-prone modules is a major problem in quality control of a software system during software development. Selecting an appropriate suggestion from various software quality classification models is a difficult decision for software project managers. In this paper, an integrated decision network is proposed to combine the well-known software quality classification models in providing the summarized suggestion. A particle swarm optimization algorithm is used to search for suitable combinations among the software quality classification models in the integrated decision network. The experimental results show that the proposed integrated decision network outperforms the independent software quality classification models. It also provides an appropriate summary for decision makers.
    Expert Syst. Appl. 01/2011; 38:4618-4625.
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    ABSTRACT: The exploration of three-dimensional (3D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a classification approach based on the hybrid use of case-based reasoning (CBR) and genetic algorithms (GAs) for hypertension detection using anthropometric body surface scanning data. The obtained result reveals the relationship between a subject’s 3D scanning data and hypertension disease. The GA is adopted to determine the appropriate feature weights for CBR. The proposed approaches were experimented and compared with a regular CBR and other widely used approaches including neural nets and decision trees. The experiment showed that applying GA to determine the suitable weights in CBR is a feasible approach to improving the effectiveness of case matching of hypertension disease. It also demonstrated that different weighted CBR approach presents better classification accuracy over the results obtained from other approaches.
    Knowledge-Based Systems. 01/2011;
  • M.L. Shih, B.W. Huang, Nan-Hsing Chiu, C. Chiu, W.Y. Hu
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    ABSTRACT: Since Taiwan joined the World Trade Organization (WTO) in 2002, pricing decision has become more essential to the development of the broiler industry. The effective prediction of broiler prices is essential from the viewpoint of the agriculture authority and the Poultry Association, thus a more realistic broiler price structure can assist the government to manage the national production resources more effectively. This research proposes a weighted case-based reasoning (CBR) approach to construct a price prediction model. The genetic algorithm model was adopted to find out the most suitable feature weights for CBR. Previous local production data and economic indices, along with information about imported chicken, were collected to build the prediction model. The experimental results indicated that the proposed CBR approach could exhibit a better prediction performance than the ones exhibited by linear regression, regression tree, and neural nets approaches. The findings also revealed that broiler prices were mostly influenced by the prices of colorful broilers and chicks.
    Computers and Electronics in Agriculture. 01/2009;
  • Sun-Jen Huang, Nan-Hsing Chiu
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    ABSTRACT: The ability to accurately and consistently estimate software development efforts is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle, are prone to a certain degree of estimation errors. A software effort estimation model which adopts a fuzzy inference method provides a solution to fit the uncertain and vague properties of software effort drivers. The present paper proposes a fuzzy neural network (FNN) approach for embedding artificial neural network into fuzzy inference processes in order to derive the software effort estimates. Artificial neural network is utilized to determine the significant fuzzy rules in fuzzy inference processes. We demonstrated our approach by using the 63 historical project data in the well-known COCOMO model. Empirical results showed that applying FNN for software effort estimates resulted in slightly smaller mean magnitude of relative error (MMRE) and probability of a project having a relative error of less than or equal to 0.25 (Pred(0.25)) as compared with the results obtained by just using artificial neural network and the original model. The proposed model can also provide objective fuzzy effort estimation rule sets by adopting the learning mechanism of the artificial neural network.
    Applied Intelligence 01/2009; 30:73-83. · 1.85 Impact Factor
  • Nan-Hsing Chiu
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    ABSTRACT: The inherent uncertainty and incomplete information of the software development process presents particular challenges for identifying fault-prone modules and providing a preferred model early enough in a development cycle in order to guide software enhancement efforts effectively. Grey relational analysis (GRA) of grey system theory is a well known approach that is utilized for generalizing estimates under small sample and uncertain conditions. This paper examines the potential benefits for providing an early software-quality classification based on improved grey relational classifier. The particle swarm optimization (PSO) approach is adopted to explore the best fit of weights on software metrics in the GRA approach for deriving a classifier with preferred balance of misclassification rates. We have demonstrated our approach by using the data from the medical information system dataset. Empirical results show that the proposed approach provides a preferred balance of misclassification rates than the grey relational classifiers without using PSO. It also outperforms the widely used classifiers of classification and regression trees (CART) and C4.5 approaches.
    Expert Syst. Appl. 01/2009; 36:10727-10734.
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    ABSTRACT: Predicting the upcoming broiler market price is important for the producers in developing their production plan. Effective price prediction model can aid producers to prevent over production or production shortage of broilers in advance. This research proposes an adapted CBR approach for predicting broiler price. The results indicate that the proposed adapted CBR approach demonstrates superior prediction performance than un-adapted CBR approach, CART, artificial neural nets and linear regression with at least 50% less of mean average error. This study finds that adjusting the price of the most similar case by considering the similarity distance to the case being predicted is a key to improve the prediction accuracy of the case-based broiler price estimation model.
    Expert Syst. Appl. 01/2009; 36:1014-1019.
  • Sun-Jen Huang, Nan-Hsing Chiu, Li-Wei Chen
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    ABSTRACT: Accurate estimates of efforts in software development are necessary in project management practices. Project managers or domain experts usually conduct software effort estimation using their experience; hence, subjective or implicit estimates occur frequently. As most software projects have incomplete information and uncertain relations between effort drivers and the required development effort, the grey relational analysis (GRA) method has been applied in building a formal software effort estimation model for this study. The GRA in the grey system theory is a problem-solving method that is used when dealing with similarity measures of complex relations. This paper examines the potentials of the software effort estimation model by integrating a genetic algorithm (GA) to the GRA. The GA method is adopted to find the best fit of weights for each software effort driver in the similarity measures. Experimental results show that the software effort estimation using an integration of the GRA with GA method presents more precise estimates over the results using the case-based reasoning (CBR), classification and regression trees (CART), and artificial neural networks (ANN) methods.
    European Journal of Operational Research 08/2008; · 2.04 Impact Factor
  • Source
    Sun-Jen Huang, Nan-Hsing Chiu, Yu-Jen Liu
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    ABSTRACT: Precision in estimating the required software development effort plays a critical factor in the success of software project management. Most existing software effort estimation models only compare the accuracies of software effort estimates from the historical data without clustering. A potential factor that can affect the accuracies of the established effort estimation models is the homogeneity of the data. However, such investigation on the effects of the accuracies of the derived effort estimates is seldom explored in software effort estimation literature. Therefore, this paper aims to explore the effects of accuracies of the software effort estimation models established from the clustered data by using the International Software Benchmarking Standards Group (ISBSG) repository. The ordinary least square (OLS) regression method is adopted to establish a respective effort estimation model in each cluster of datasets. The empirical experiment results show that the estimation accuracies do not reveal significant differences within the respective dataset clustered by each software effort driver. It also demonstrates that software effort estimation models from the clustered data present almost similar accuracy results compared to models from the entire data without clustering.
    Information and Software Technology. 08/2008;
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    Nan-Hsing Chiu, Sun-Jen Huang
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    ABSTRACT: Analogy-based estimation is a widely adopted problem solving method that has been evaluated and confirmed in software effort or cost estimation domains. The similarity measures between pairs of projects play a critical role in the analogy-based software effort estimation models. Such a model calculates a distance between the software project being estimated and each of the historical software projects, and then retrieves the most similar project for generating an effort estimate. Although there exist numerous analogy-based software effort estimation models in literature, little theoretical or experimental works have been reported on the method of deriving an effort estimate from the adjustment of the reused effort based on the similarity distance. The present paper investigates the effect on the improvement of estimation accuracy in analogy-based estimations when the genetic algorithm method is adopted to adjust reused effort based on the similarity distances between pairs of projects. The empirical results show that applying a suitable linear model to adjust the analogy-based estimations is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the proposed model is comparable with those obtained when using other effort estimation methods.
    Journal of Systems and Software. 01/2007;
  • Chaochang Chiu, Pei-Lun Hsu, Nan-Hsing Chiu
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    ABSTRACT: Reliable and effective maintenance support is vital to the airline operations and flight safety. This research proposes the hybrid of apriori algorithm and constraint-based genetic algorithm (ACBGA) approach to discover a classification tree for electronic ballasts troubleshooting. Compared with a simple GA (SGA) and the Apriori algorithms with GA (AGA), the ACBGA achieves higher classification accuracy for electronic ballast data.
    Advances in Natural Computation, Second International Conference, ICNC 2006, Xi'an, China, September 24-28, 2006. Proceedings, Part I; 01/2006
  • Sun-Jen Huang, Nan-Hsing Chiu
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    ABSTRACT: A reliable and accurate estimate of software development effort has always been a challenge for both the software industry and academia. Analogy is a widely adopted problem solving technique that has been evaluated and confirmed in software effort or cost estimation domains. Similarity measures between pairs of effort drivers play a central role in analogy-based estimation models. However, hardly any research has addressed the issue of how to decide on suitable weighted similarity measures for software effort drivers. The present paper investigates the effect on estimation accuracy of the adoption of genetic algorithm (GA) to determine the appropriate weighted similarity measures of effort drivers in analogy-based software effort estimation models. Three weighted analogy methods, namely, the unequally weighted, the linearly weighted and the nonlinearly weighted methods are investigated in the present paper. We illustrate our approaches with data obtained from the International Software Benchmarking Standards Group (ISBSG) repository and the IBM DP services database. The experimental results show that applying GA to determine suitable weighted similarity measures of software effort drivers in analogy-based software effort estimation models is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the nonlinearly weighted analogy method presents better estimate accuracy over the results obtained using the other methods.
    Information and Software Technology. 01/2006;
  • Source
    Sun-Jen Huang, Chieh-Yi Lin, Nan-Hsing Chiu
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    ABSTRACT: As software cost drivers are fuzzy and uncertain, software cost estimates are prone to a certain degree of estimation errors especially in their early stages of software devel- opment life cycle. However, most of the existing software cost estimation models in pre- sent literature only generate a single point estimate and do not explicitly reveal the de- gree of risks caused by their inaccuracies. This paper proposes a fuzzy decision tree ap- proach for embedding risk assessment information into a software cost estimation model. Using this model, one may be able to determine the software cost estimate as well as the estimation error in the form of a fuzzy set. In verifying the merits of this model, we have used the 63 historical project data in the COCOMO model. The validation result shows that our proposed model reveals the risk assessment of the generated software cost esti- mate, and at the same time yields an even more accurate result as compared to the origi- nal COCOMO model.
    J. Inf. Sci. Eng. 01/2006; 22:297-313.
  • Chaochang Chiu, Pei-Chann Chang, Nan-Hsing Chiu
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    ABSTRACT: Owing to the complexity of wafer fabrication, the traditional human approach to assigning due-date is imprecise and very prone to failure, especially when the shop status is dynamically changing. Therefore, assigning a due date to each order becomes a challenge to the production planning and scheduling staff. Since most production orders are similar to those previously manufactured, the case based reasoning (CBR) approach provides a suitable means for solving the due-date assignment problem. This research proposes a CBR approach that employs the k-nearest neighbors concept with dynamic feature weights and non-linear similarity functions. The test results show that the proposed approach can more accurately predict order due dates than other approaches.
    Journal of Intelligent Manufacturing 05/2003; 14(3):287-296. · 1.28 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The exploration of three-dimensional (3D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a classification approach based on the hybrid of the case-based reasoning (CBR) and genetic algorithms (GAs) approach for hypertension detection using anthropometric body surface scanning data. The experiment showed that our proposed approach is able to improve the effectiveness of case matching of hypertension disease.
    01/1970: pages 255-263;

Publication Stats

228 Citations
6.77 Total Impact Points

Institutions

  • 1970–2011
    • Ching Yun University
      Taoyuan City, Taiwan, Taiwan
  • 2006–2007
    • National Taiwan University of Science and Technology
      • Department of Information Management
      Taipei, Taipei, Taiwan
  • 2003–2006
    • Yuan Ze University
      • Department of Information Management
      Taoyuan City, Taiwan, Taiwan