• Communications for Statistical Applications and Methods
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• 제28권1호
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• pp.89-97
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• 2021

Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.75-81
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• 2019

Though recent advancement of deep learning methods have provided satisfactory results from large data domain, somehow yield poor performance on few-shot classification tasks. In order to train a model with strong performance, i.e. deep convolutional neural network, it depends heavily on huge dataset and the labeled classes of the dataset can be extremely humongous. The cost of human annotation and scarcity of the data among the classes have drastically limited the capability of current image classification model. On the contrary, humans are excellent in terms of learning or recognizing new unseen classes with merely small set of labeled examples. Few-shot learning aims to train a classification model with limited labeled samples to recognize new classes that have neverseen during training process. In this paper, we increase the backbone depth of the embedding network in orderto learn the variation between the intra-class. By increasing the network depth of the embedding module, we are able to achieve competitive performance due to the minimized intra-class variation.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.635-644
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• 2019

Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.

• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.565-574
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• 2004

Classification tree algorithms including as CART by Brieman et al.(1984) in some aspects, recursively partition the data space with the aim of making the distribution of the class variable as pure as within each partition and consist of several steps. SUPPORT(smoothed and unsmoothed piecewise-polynomial regression trees) method of Chaudhuri et al(1994), a weighted averaging technique is used to combine piecewise polynomial fits into a smooth one. We focus on applying SUPPORT to a binary class variable. Logistic model is considered in the caculation techniques and the results are shown good classification rates compared with other methods as CART, QUEST, and CHAID.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.447-450
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• 2004

This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

• 한국지진공학회논문집
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• 제11권1호
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• pp.59-65
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• 2007

IBC와 KBC의 지반분류는 ft-kips 단위체계를 기본으로 하고, 지반종류를 단일 지반특성값이 아닌 지반특성값 범위로 규정하여 지반종류에 따른 전단파속도와 지반계수들 간의 불명확한 관계 때문에 지반계수의 선형보간이 쉽지 않다. 또한, KBC의 지반분류에서 각 지반종류에 대한 지반특성값 범위가 너무 넓어서 구조기술자들이 다양한 지반의 실제적인 지반계수를 추정하는데 어려움을 격고 있다. 이 연구에서는 SI 단위체계를 고려한 새로운 지반분류체계를KBC등 차세대 내진설계기준을 위해 제안하였고, 제안된 새로운 지반분류에 따라 지반계수들의 선형보간 가능성을 검토하기 위해 $F_{a},\;F_{v}$, 지반계수들의 비교에 관한 연구를 수행하였다. 연구결과에 의하면, SI 단위체계와 얕게 묻힌기초 밑 30m 지반의 지반특성을 고려한 새로 제안한 지반분류체계를 이용하는 것이 지반계수의 선형보간을 위해서 보다 합리적이고, 설계스펙트럼 가속도계수의 선형보간도 각 지반을 대표하는 전단파속도에 따라 지반계수를 규정함으로써 보다 합리적으로 수행할 수 있다. 연구결과에 따라 KBC 내진설계기준을 위한 새로운 지반분류체계와 선형보간이 가능한 설계스펙트럼 가속도 계수를 제안하였다.

• 대한치과의사협회지
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• 제15권2호
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• pp.107-110
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• 1977

Class I malocclusion is essentially a dental dysplasia. Rotations, individual tooth malpositions, missing teeth, tooth size discrepancies, etc., fall under this classification. There are two types of class I malocclusions. One is identified by and insufficient denture base to accommodate the teeth; the other has more denture base than tooth material, creating spaces in the arch. The tooth material-to denture base discrepancies may be slight, calling for only a little increase in arch length for alignment and the correction of minor rotations. Discrepancies may also be great, in which case it becomes necessary to reduce tooth material by extraction, so as to make the tooth material more in proportion to the size of the denture base. The author had attempted orthodontic treatment of a class I malocclusion case of 13-year old boy in which high canines and impacted mandibular second premolars were involved. The author obtained good results.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 하계종합학술대회 논문집(1)
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• pp.179-182
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• 2004

The process of categorizing packets into 'class' in an Internet router is called packet classification. All packets with same class obey predefined rule specified in routing tables. Performing classification in real time on an arbitrary number of fields is a very challenge task. In this paper, we present a new algorithm named EnBiT-PC (EnBiT Packet Classification). and evaluate its performance against real classifiers in use today. We compare with previous algorithms, and found out that EnBiT-PC classify packets very efficiently and has relatively small storage requirements.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 제14회 신호처리 합동 학술대회 논문집
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• pp.619-622
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• 2001

In this paper, an efficient approach to classification of transient and harmonic disturbances in power systems is proposed. First, the Stop-and-Go CA CFAR Detector is utilized to detect a disturbance from the power signals which are mixed with other disturbances and noise. Then, (i) Wigner Distribution, SVD(Singular Value Decomposition) and Fisher´s Criterion (ii) DWT and Fisher´s Criterion, are applied to extract an efficient feature vector. For the classification procedure, a combined neural network classifier is proposed to classify each corresponding disturbance class. Finally, the 10 class data simulated by Matlab power system blockset are used to demonstrate the performance of the proposed classification system.

• 한국측량학회지
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• 제33권4호
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• pp.267-275
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• 2015

토지피복지도는 환경, 군사, 의사결정 등 다양한 분야에서 널리 사용되고 있다. 본 연구에서는 단일 위성영상과 환경부에서 제공하는 국가토지피복도를 이용하여 훈련자료를 자동으로 추출하고, 이를 활용하여 피복을 분류하는 방법을 제안하였다. 이를 위하여 초기 훈련자료는 무감독분류인 ISODATA와 기존 토지피복도를 이용하였으며, 무감독 분류 사용시 각 클래스별 분류 선정과 클래스 명명, 감독분류에서 훈련자료 선정 등의 문제점을 해결하기 위하여 기존 토지피복도의 클래스 정보를 활용하여 자동으로 클래스를 분류하고 명명하였다. 추출된 초기 훈련자료는 대상 위성영상의 토지피복분류를 위하여 MLC의 훈련자료를 활용하였고, 피복분류의 정확도 향상을 위하여 반복방법을 적용하여 훈련자료를 갱신하였으며 최종적으로 토지피복지도를 추출하였다. 또한, 화소분류방법에서 발생하는 salt and pepper를 감소시키기 위하여 각 반복단계별 MRF를 적용하여 분류정확도를 향상시켰다. 본 연구에서 제안된 방법을 대상지역에 적용한 결과 효과적으로 토지피복지도를 생성할 수 있음을 정량적, 시각적으로 확인하였다.