• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.161-169
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• 2010

The requirement elicitation is the task of eliciting requirements from needs of stakeholders, and preparing for information for negotiation. However, there are methods for gathering needs, but there is no specific method for classifying them, generating information for negotiation. Therefore, To solve the problems, this paper proposes a method to classify requirement and to generate information for negotiation. The proposed methods use Kano model, statistical technique, and identifying relationship between requirements and problems within scope. Finally, we validate the proposed method on simulations, Rough Set Theory, and case study of model.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.185-191
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• 2011

In the credit evaluation study with the assumption of mixture distributions, the ROC curve is a useful method to explore the discriminatory power of default and non-default borrowers. The AROC curve is an adjusted ROC curve that can be identified with the corresponding score and is mathematically analyzed in this work. We obtain patterns of this curve by applying normal distributions. Moreover, the relationship between the AROC curve and many classification accuracy statistics are explored to find the optimal threshold. In the case of equivalent variances of two distributions, we obtain that the local minimum of the AROC curve is estimated at the optimal threshold to maximize certain classification accuracies.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.979-993
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• 2021

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). In this study, we considered the problem that ASC faces in real-world applications that the model used should have low-complexity. We compared several models that apply light-weight techniques. First, a base CNN model was proposed using log mel-spectrogram, deltas, and delta-deltas features. Second, depthwise separable convolution, linear bottleneck inverted residual block was applied to the convolutional layer, and Quantization was applied to the models to develop a low-complexity model. The model considering low-complexity was similar or slightly inferior to the performance of the base model, but the model size was significantly reduced from 503 KB to 42.76 KB.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1924-1929
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• 2007

Nowadays, a lot of related data obtained from these research could be given a new present meaning to accomplish the original purpose of the whole research as a human project. The method of tumor classification based on microarray could contribute to being accurate tumor classification by finding differently expressing gene pattern statistically according to a tumor type. Therefore, the process to select a closely related informative gene with a particular tumor classification to classify tumor using present microarray technology with effect is essential. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer, constructed accurate tumor classification model by extracting informative gene list through normalization separately and then did performance estimation by analyzing and comparing each of the experiment results. Result classifying Multi-Perceptron classifier for selected genes using Pearson correlation coefficient represented the accuracy of 95.6%.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.99-104
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• 2002

cDNA 마이크로어레이자료를 이용한 분류방법은 수많은 유전자의 발현을 동시에 모니터링 할 수 있으므로 특정 질병간의 분자생물학적 변이를 이해하는데 있어 기존의 분류방법보다 신뢰성이 훨씬 높을 것으로 기대되고 있다 최근에 Dudoit et al.(2001)은 cDNA 마이크로어레이를 이용한 유전자발현자료의 분석에 있어 분류를 위한 여러 고전적인 판별분류기법 및 최근에 개발된 기법들을 비교, 평가하였다. 본 논문에서는 Dudoit et al.(2001)에서 다루지 않았던 많은 최신 기법들을 포함하여 인간의 종양 자료뿐만이 아니라 농작물을 포함한 동식물 자료에 적용하여 보다 폭넓은 비교연구를 하였다.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.911-921
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• 2009

Receiver Operating Characteristic(ROC) and Cumulative Accuracy Profile(CAP) curves are two methods used to assess the discriminatory power of different credit-rating approaches. The points of optimal classification accuracy on an ROC curve and of maximal profit on a CAP curve can be found by using iso-performance tangent lines, which are based on the standard notion of accuracy. In this paper, we offer an alternative accuracy measure called the true rate. Using this rate, one can obtain alternative optimal threshold points on both ROC and CAP curves. For most real populations of borrowers, the number of the defaults is much less than that of the non-defaults, and in such cases the true rate may be more efficient than the accuracy rate in terms of cost functions. Moreover, it is shown that both alternative scores of optimal classification accuracy and maximal profit are the identical, and this single score coincides with the score corresponding to Kolmogorov-Smirnov statistic used to test the homogeneous distribution functions of the defaults and non-defaults.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.519-527
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• 2016

This research aims at investigating the measures of agreement between BMI classification standard and that of 9-point contour drawing rating scale(CDRS), verifying their usefulness for the application to the filed, examining university students' substantial understanding of their bodies, and offering correct information regarding the distorted recognition of their bodies. In order to examine the measures of agreement between the classification standard of BMI and that of CDRS, and the women university students' recognition of their body images depending on BMI, Cross tabulation was carried out, and ${\chi}^2$, Spearman rank correlation coefficient and kappa statistics were calculated. As the analysis results, the classification standards of CDRS and BMI judged by general female college students showed statistically the correlation was high with ${\rho}=.719$(p<.001) and an average level of confirmity with ${\kappa}=.506$(p<.001). Based on these results, regarding body shape, sizes and shapes according to racial characteristics need to be controlled later.

• Journal of the Korean Statistical Society
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• v.10
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• pp.81-82
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• 1981

허문열 박사에 의하여 분류된 EDP의 3단계에 대하여 전적으로 동감하며, 우리나라가 현재 초기단계를 벗어나 성장단계에 접어들고 있는 것도 사실이라고 하겠다. 또한 조만간 (5-6년후)에 성숙단계에 접어들기 시작하면 컴퓨터에 의하여 처리되는 통계 package의 사용이 급증하고 이를 다룰 줄 아는 통계인의 수요가 매우 크리라고 믿어진다. 통계를 다루는 학자 또는 실무자들은 사회에서 요구하는 통계서비스(statistical consulting service)를 할 수 있는 것이 바람직하고, 우리의 역할을 다 할 수 있도록 우리 스스로를 준비시켜야 할 것이다. 사회에 통계를 보급시키고 통계의 활용을 더욱 촉진시키기 위하여 다음의 몇가지를 제안하는 바이다. 이들은 또한 허문열 박사의 주제논문에서도 암시되고 있는 방향이라고도 하겠다.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.447-455
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• 2000

We propose a weighted kernel pattern recognition method using the K -means clustering algorithm to reduce computation and storage required for the full kernel classifier. This technique finds a set of reference vectors and weights which are used to approximate the kernel classifier. Since the hierarchical clustering method implemented in the 'Weighted Parzen Window (WP\V) classifier is not able to rearrange the proper clusters, we adopt the K -means algorithm to find reference vectors and weights from the more properly rearranged clusters \Ve find that the proposed method outperforms the \VP\V method for the repre~entativeness of the reference vectors and the data reduction.