• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.471-483
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• 1998

The influence of observations on the misclassification probability in multiple discriminant analysis under the equal covariance assumption is investigated by the local influence method. Under an appropriate perturbation we can get information about influential observations and outliers by studying the curvatures and the associated direction vectors of the perturbation-formed surface of the misclassification probability. We show that the influence function method gives essentially the same information as the direction vector of the maximum slope. An illustrative example is given for the effectiveness of the local influence method.

• Journal of the Korean Statistical Society
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• 제25권1호
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• pp.145-151
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• 1996

The local behaviour of the surface formed by the perturbed maximum likelihood estimator of the squared Mahalanobis distance is investigated. The study of the local behaviour allows a simultaneous perturbation on the samples of interest and it is effective in identifying influential observations.

• ETRI Journal
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• 제16권1호
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• pp.35-43
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• 1994

In this paper, the robustness of the artificial neural networks to noise is demonstrated with a multilayer perceptron, and the reason of robustness is due to the statistical orthogonality among hidden nodes and its hierarchical information extraction capability. Also, the misclassification probability of a well-trained multilayer perceptron is derived without any linear approximations when the inputs are contaminated with random noises. The misclassification probability for a noisy pattern is shown to be a function of the input pattern, noise variances, the weight matrices, and the nonlinear transformations. The result is verified with a handwritten digit recognition problem, which shows better result than that using linear approximations.

• 대한안전경영과학회지
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• 제11권3호
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• pp.189-198
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• 2009

This thesis studies two imputation methods, the MCMC method and the EM algorithm, that take care of the problem. The performance of the two methods for the linear (or quadratic) discriminant analysis are evaluated under various types of incomplete observations. Based on simulated experiments, the effect of the imputation using the EM algorithm and the MCMC method are evaluated and compared in terms of the probability of misclassification and the RMSE. This is done for the various cases of incomplete observations. The cases are differentiated by missing rates, sample sizes, and distances between two classification groups. The studies show that the probability of misclassification and the RMSE of the EM algorithm method is lower than the MCMC method. Therefore the imputation using the EM algorithm is more efficient than the MCMC method. And the probability of misclassification of the method that all vectors of observations with missing values are omitted from analysis is lower than the EM algorithm and the MCMC method when the samples size is small and the rate of missing values is extremely big.

• Journal of the Korean Data and Information Science Society
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• 제22권2호
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• pp.149-157
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• 2011

본 연구는 신용평가 과정에서 발생하는 미결정자를 결측자료 문제로 간주하여 MAR와 MNAR 가정 하에서 추론한다. MAR 가정에서 미결정자 추론은 결정자들에 대한 로지스틱 회귀모형의 회귀 계수벡터를 이용하여 미결정자의 부도 확률을 구한 후 결정자의 부도확률과 비교하여 미결정자의 미래 상태를 판단한다. 그리고 MNAR 가정에서의 미결정자 추론은 특성변수가 추가한 로지스틱 모형으로부터 미결정자의 부도확률을 구하고 미결정자를 예측하는 방법을 제안하였다. 두 종류의 실제 자료에 대하여 모의실험을 한 결과, MAR 가정에서 미결정자의 비율이 증가하더라도 원자료의 오분류율과 추론한 결과 차이가 없으며, MNAR 가정에서는 추가적인 변수를 고려하여 미결정자를 추정하였기 때문에 미결정자의 오분류율이 MAR 가정에서의 오분류율보다 감소하고 나아가 전체에서 미결정자가 차지하는 비율이 증가함에 따라 전체의 오분류율이 더욱 감소함을 발견하였다.

• 응용통계연구
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• 제24권5호
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• pp.941-949
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• 2011

본 논문에서는 세 집단만을 판별분석 할 경우에 계산되는 오분류확률에 영향을 미치는 이상치 판별을 목적으로 하며, 쉽게 응용 가능한 간단한 영향함수식을 제시하였다. 그리고 제시된 수식을 이용하여 안면 데이터로 세 가지 사상체질을 분류해보고 각 관찰값들의 오분류확률에 대한 영향함수를 계산하였다. 이상치를 제거하고 재 판별분석을 하는 데 있어, 오분류확률에 대한 영향함수를 이용하는 것이 효율적인 방법임을 확인하였다.

• 전자공학회논문지C
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• 제34C권2호
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• pp.71-79
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• 1997

In this paper, we propose a new nearest-neighbor classifier with reduced computational complexity in search process. In the proposed classifier, the classes are divided into two sets: reference and non-reference sets. It reduces computational requriement by approximating the distance between the input and a class iwth the information of distances among the calsses. It calculates only the distance between the input and the reference classes. We convert a given classifier into RCC (reduced computational complexity but smal lincrease in misclassification probability of its corresponding RCC classifier. We designed RCC classifiers for the recognition of digits from the NIST database. We obtained an RCC classifier with 60% reduction in the computational complexity with the cost of 0.5% increase in misclassification probability.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제13권1호
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• pp.55-72
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• 2009

A misclassified size-biased modified power series distribution (MSBMPSD) where some of the observations corresponding to x = c + 1 are misclassified as x = c with probability $\alpha$, is defined. We obtain its recurrence relations among the raw moments, the central moments and the factorial moments. Discussion of the effect of the misclassification on the variance is considered. To illustrate the situation under consideration some of its particular cases like the size-biased generalized negative binomial (SBGNB), the size-biased generalized Poisson (SBGP) and sizebiased Borel distributions are included. Finally, an example is presented for the size-biased generalized Poisson distribution to illustrate the results.

• 한국통신학회논문지
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• 제19권6호
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• pp.1004-1015
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• 1994

통계적 모먼트(statistical moments)에 의한 변조형태 분류기(classifier)는 PSK 신호를 분류하는데 자주 이용되어 왔다. 이전에 사용된 분류기는 수신된 신호로부터 추출하기 어려운 신호위상 샘플의 통계적 모먼트를 이용하였으나, 본 논문에서는 확률변수변환을 통한 복조된 신호의 모먼트를 이용하여 PSK 신호를 분류하기 위한 새로운 분류기를 제안한다. 복조된 신호는 종래의 방법으로 쉽게 추출이 될 수 있다. PSK 신호에 대해 제안된 분류기의 성능평가는 복조된 신호의 정확한 위상분포를 사용하여 가산성 백색가우스잡음(AWGN)하에서 오분류확률(probability of misclassification)로 분석하였다. 분석결과 동기 시스팀이 비동기 시스팀보다 n이 4이고 오분류확률이 10 일때 BPSK에 있어서는 4dB, QPSK에 있어서는 3dB 더 우수함을 알 수 있었다.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.453-470
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• 1995

We describe a constrained optimal classification rule for the case when the prior probability of an observation belonging to one of the two populations is unknown. This is done by suggesting a balanced design for the classification experiment and constructing the optimal rule under the balanced design condition. The rule si characterized by a constrained minimization of total risk of misclassification; the constraint of the rule is constructed by the process of equation between Kullback-Leibler's directed divergence measures obtained from the two population conditional densities. The efficacy of the suggested rule is examined through two-group normal classification. This indicates that, in case little is known about the relative population sizes, dramatic gains in accuracy of classification result can be achieved.