• International journal of advanced smart convergence
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• v.8 no.4
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• pp.130-137
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• 2019

We conducted this study to identify the composition factors for consumers' selection attributes of accommodation applications and to identify the differences in the selection attributes perception of accommodation applications based on demographic characteristics and use status. According to the study, 6 factors were derived as the components of the selectivity of accommodation application and were named convenience, interactivity, economic efficiency, transaction reliability, product reliability and informativeness. And the respondents differed in their selection attributes perception of the accommodation application they used. In particular, it was found that the highest perception of informativeness and interactivity, and the lowest perception of product reliability. Finally, there were differences in the selection attributes perception of the accommodation application they used by demographic characteristics and use status. Based on the results of this study, we should strive to derive the effective marketing strategy needed for travel industry-related companies.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1653-1660
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• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

• Journal of the Korean Statistical Society
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• v.21 no.1
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• pp.1-13
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• 1992

This article deals with variable selection problem under a newly formed predictive heteroscedastic discriminant rule that accounts for mulitple homogeneous covariance matrices across the K multivariate normal populations. A general version of predictive discriminant rule, a variable selection criterion, and a criterion for stopping with further selection are suggested. In a simulation study the practical utilities of those considered are demonstrated.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.187-194
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• 2014

We consider a Bayesian nonignorable selection model to accommodate the selection bias. Markov chain Monte Carlo methods is known to be very useful to fit the nonignorable selection model. However, sensitivity to prior assumptions on parameters for selection mechanism is a potential problem. To quantify the sensitivity to prior assumption, the deviance information criterion and the conditional predictive ordinate are used to compare the goodness-of-fit under two different prior specifications. It turns out that the 'MLE' prior gives better fit than the 'uniform' prior in viewpoints of goodness-of-fit measures.

• 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.

• 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.

• Journal of Crop Science and Biotechnology
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• v.11 no.4
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• pp.263-268
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• 2008

Bacterial leaf pustule (BLP) caused by Xanthomonas axonopodis pv. glycines is a serious disease to make pustule and chlorotic haloes in soybean [Glycine max (L). Merr.]. While inheritance mode and map positions of the BLP resistance gene, rxp are known, no sequence information of the gene was reported. In this study, we made five near isogenic lines (NILs) from separate backcrosses (BCs) of BLP-susceptible Hwangkeumkong $\times$ BLP-resistant SS2-2 (HS) and BLP-susceptible Taekwangkong$\times$ SS2-2 (TS) through foreground and background selection based on the four-stage selection strategy. First, 15 BC individuals were selected through foreground selection using the simple sequence repeat (SSR) markers Satt486 and Satt372 flanking the rxp gene. Among them, 11 BC plants showed the BLP-resistant response. The HS and TS lines chosen in foreground selection were again screened by background selection using 118 and 90 SSR markers across all chromosomes, respectively. Eventually, five individuals showing greater than 90% recurrent parent genome content were selected in both HS and TS lines. These NILs will be a unique biological material to characterize the rxp gene.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.12
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• pp.1690-1694
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• 2002

Genetic parameters were estimated for birth weight and weaning weight from three year (1991-1993) data totalling 1100 records of 25 rams to 205 ewes of Indigenous Sabi flock maintained at Grasslands Research Station in Zimbabwe. AIREML procedures were used fitting an Animal Model. The statistical model included the fixed effects of year of lambing, sex of lamb, birth type and the random effect of ewe. Weight of ewe when first joined with ram was included as a covariate. Direct heritability estimates of 0.27 and 0.38, and maternal heritability estimates of 0.24 and 0.09, were obtained for birth weight and weaning weight, respectively. The total heritability estimates were 0.69 and 0.77 for birth weight and weaning weight, respectively. Direct-aternal genetic correlations were high and positive. The corresponding genetic covariance estimates between direct and maternal effects were positive and low, 0.25 and 0.18 for birth weight and weaning weight, respectively. Responses to selection were 0.8 kg and 0.14 kg for birth weight and weaning weight, respectively. The estimated expected correlated response to selection for birth weight by directly selecting for weaning weight was 0.26. Direct heritabilities were moderate; as a result selection for any of these traits should be successful. Maternal heritabilities were low for weaning weight and should have less effect on selection response. Indirect selection can give lower response than direct selection.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.131-139
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• 2013

Support vector machines(SVM) may perform poorly in the presence of noise variables; in addition, it is difficult to identify the importance of each variable in the resulting classifier. A feature selection can improve the interpretability and the accuracy of SVM. Most existing studies concern feature selection in the linear SVM through penalty functions yielding sparse solutions. Note that one usually adopts nonlinear kernels for the accuracy of classification in practice. Hence feature selection is still desirable for nonlinear SVMs. In this paper, we compare the performances of nonlinear feature selection methods such as component selection and smoothing operator(COSSO) and kernel iterative feature extraction(KNIFE) on simulated and real data sets.

• The Korean Journal of Food & Health Convergence
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• v.7 no.4
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• pp.1-11
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• 2021

The purpose of this study is to investigate the ramen selection attributes of consumers. This research assigned taste, price, quantity, design, and brand as selection attributes, all of which have already been verified by previous studies as selection attributes when purchasing processed foods. A total of 500 questionnaires were issued, and the survey results were analysed to ensure validity and reliability. A Structural Equation Model was used to test the hypotheses of the study. Based on the analysis, taste, price, quantity, design, and brand had a statistically significant effect on satisfaction. Furthermore, satisfaction had a statistically significant effect on repurchase intention. Among the selection attributes (taste, price, quantity, design, and brand), only price had a statistically significant effect on repurchase intention. However, the influence of the selection attributes on satisfaction varied depending on the consumer's consumption value. In order to analyse the moderating effect of consumption value, the respondent group was divided into a hedonism group and pragmatism group, and analysed. It empirically proved that the hedonistic value-oriented group valued taste, and the practical value-oriented group valued price the most. This study empirically verified the relationship between ramen selection attributes and consumption value, and provided corresponding theoretical and practical implications.