• Korean Management Science Review
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• v.21 no.1
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• pp.113-129
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• 2004

Nowadays many enterprises build e-procurement systems. An e-procurement is a Web-based procurement process and its functionalities are considered important in the B2B e-commerce. Buyers should select competent suppliers for a successful e-procurement. Therefore, this study proposes a method using the analytic hierarchy process(AHP) for building a Web-based supplier selection system. In detail, the purpose of this study is (1) to review methods previously used when buyers selecting suppliers and to extract important selection criteria： (2) to explain extended AHP method adopted by this study among supplier selection methods： (3) to describe the supplier selection steps using extended AHP ： and (4) to propose a decision support system embedding the methodology described above. The proposed system comprises of three phase： first phase is to evaluate suppliers on enterprise level： second phase to evaluate them on each transaction level： third phase to post-evaluate them.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.234-239
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• 2014

This paper presents an empirical study on selecting a small amount of useful unlabeled data to improve the classification accuracy of semi-supervised learning algorithms. In particular, a hybrid method of unifying the simply recycled selection method and the incrementally-reinforced selection method was considered and evaluated empirically. The experimental results, which were obtained from well-known benchmark data sets using semi-supervised support vector machines, demonstrated that the hybrid method works better than the traditional ones in terms of the classification accuracy.

• Culinary science and hospitality research
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• v.23 no.5
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• pp.34-47
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• 2017

This study is designed to figure out the effects of major selection motivation on career efficacy and major satisfaction of college students majoring in culinary and foodservice management. To achieve this purpose, a survey was carried out to 209 college students. The findings showed that their major selection had a significant effect on their career efficacy and major satisfaction. But the mediating effect of their career efficacy as a mediator - which improves their major selection and major satisfaction - was partially adopted. Consequently, their internal and external participation motivation for their major selection is a facilitating mechanism to maximize their major satisfaction, and it is necessary to limit the use as a mediating variable of their career efficacy.

• Culinary science and hospitality research
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• v.9 no.3
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• pp.37-54
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• 2003

As the life style of modern people is gradually being more scientific, up-to-date and specialized, food habit and food culture are a measure of cultural level of a country. Studies on consumer behavioral model show that food habit is closely related to consumer preference, changing life pattern and increasing family income. The purpose of this study was, accordingly, to define the impact of menu characteristics on customer menu selection. For that purpose, some attempts were made: First, discuss the theories on Italian food and customer purchasing behavior as a standard of analysis. Second, find out if there are any differences in customer menu-selection factors in hotel Italian restaurant. Third, make an empirical analysis of menu-selection factors in hotel Italian restaurant to suggest in which direction it should move forward. Fourth, analyze the relationship of demographic characteristics to menu-selection factors.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.147-157
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• 2015

We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.

• Animal cells and systems
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• v.1 no.4
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• pp.619-627
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• 1997

Maintenance of polymorphism in a two-locus system with two alleles under stabilizing selection has been tested by Monte-Carlo simulation. The effect of each allele was additive. Only gene x environment interactions and degree of genetic linkage between loci were considered. There were no other evolutionary forces acting except stabilizing selection. Fixation rates were influenced by the extent of environmental change and the degree of genetic linkage. In most cases, stabilizing selection depleted genetic variability when two loci have a lower degree of linkage (10 cM). When two loci are closely linked (0.1 cM), however, stabilizing selection promoted balanced heterozygotes in changing environments. Thus, environment-dependent selection and recombination rate are important parameters which should be incorporated into mechanisms of maintenance of genetic variability.

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.47-53
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• 2009

We define product line (or mix) selection problem as selecting a subset of potential product variants that can simultaneously minimize product proliferation and maintain market coverage. Selecting the most efficient product mix is a complex problem, which requires analyses of multiple criteria. This paper proposes a method based on Data Envelopment Analysis (DEA) for product line selection. Data Envelopment Analysis (DEA) is a linear programming based technique commonly used for measuring the relative performance of a group of decision making units with multiple inputs and outputs. Although DEA has been proved to be an effective evaluation tool in many fields, it has not been applied to solve the product line selection problem. In this study, we construct a five-step method that systematically adopts DEA to solve a product line selection problem. We then apply the proposed method to an existing line of staplers to provide quantitative evidence for managers to generate desirable decisions to maximize the company profits while also fulfilling market demands.

• Genomics & Informatics
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• v.2 no.1
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• pp.19-29
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• 2004

Gene expression profiles may offer more information than morphology and provide an alternative to morphology- based tumor classification systems. Informative gene selection is finding gene subsets that are able to discriminate between tumor types, and may have clear biological interpretation. Gene selection is a fundamental issue in gene expression based tumor classification. In this report, techniques for selecting informative genes are illustrated and supervised shaving introduced as a gene selection method in the place of a clustering algorithm. The supervised shaving method showed good performance in gene selection and classification, even though it is a clustering algorithm. Almost selected genes are related to leukemia disease. The expression profiles of 3051 genes were analyzed in 27 acute lymphoblastic leukemia and 11 myeloid leukemia samples. Through these examples, the supervised shaving method has been shown to produce biologically significant genes of more than $94\%$ accuracy of classification. In this report, SVM has also been shown to be a practicable method for gene expression-based classification.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.