• 한국정보통신학회논문지
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• 제16권12호
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• pp.2785-2791
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• 2012

본 논문에서는 학습 콘텐츠의 효과적인 관리와 재사용을 위한 학습객체 자동 분류 기법을 제안한다. 제안한 기법은 학습객체들의 발생 사례를 이용하여 학습객체들의 응집도를 생성하고, 응집도를 기반으로 학습객체들의 연관성을 측정하여 학습객체들의 자동 분류를 수행한다. 제안한 기법을 학습관리시스템에 적용하면 학습 콘텐츠의 개발 비용을 절감시킬 수 있는 장점이 있다. 시뮬레이션에서 확률 기반 기법의 평균 정확도는 28.20%로 나타났고, 응집도 기반 기법의 평균 정확도는 56.38%로 나타났다. 시뮬레이션을 통해 본 논문에서 제안한 기법이 학습객체 자동 분류에 효과적이라는 것을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.931-940
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• 2011

분류분석은 학습표본으로부터 분류규칙을 도출한 후 새로운 표본에 적용하여 특정 범주로 분류하는 방법이다. 데이터의 복잡성에 따라 다양한 분류분석 방법이 개발되어 왔지만, 데이터 차원이 높고 변수간 상관성이 높은 경우 정확하게 분류하는 것은 쉽지 않다. 본 연구에서는 데이터차원이 상대적으로 높고 변수간 상관성이 높을 때 강건한 분류방법을 제안하고자 한다. 부분최소자승법은 연속형데이터에 사용되는 기법으로서 고차원이면서 독립변수간 상관성이 높을 때 예측력이 높은 통계기법으로 알려져 있는 다변량 분석기법이다. 벌점 부분최소자승법을 이용한 분류방법을 실제데이터와 시뮬레이션을 적용하여 성능을 비교하고자 한다.

• 한국한의학연구원논문집
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• 제3권1호
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• pp.153-167
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• 1997

Conventionally, identification and classification methods of natural products include the morphological survey and assay of chemical disposition, sing these methods, however, is not satisfying for the precise identification of natural products because they are often valiable in the compositions and morphology To standardize the natural products identification and classification, genomic DNA analysis such as RAPD, RFLP and Amp-FLP can be adopted for this purpose. In this study, various ginsengs and bear gall bladder were tested for the development of genetic identification and classification method. Varieties of ginsengs such as, P. ginseng, P. quinquefolium, P. japonicus and P. notoginseng, were genetically analyzed by RAPD. Also, DNA isolated from Bear blood and gall bladder, Ursus thibetanus, Ursus americanus and Ursus arctos, were analyzed by the same method. The results demonstrated that the identification and classification of bear gall bladder and various ginsengs were possible by RAPD analysis. Therefore, this method was thought to be used as a additional method for the identification and classification of other natural products.

• 한국음향학회지
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• 제19권3호
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• pp.20-25
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• 2000

본 논문에서는 3차원 물체 인식을 위한 표면 분류 및 임계치 선정 방법에 대해 제안 하고자 한다. 3차원 영상 처리는 크게 거리 영상의 획득과 특징 추출 그리고 정합 과정으로 이루어진다. 본 논문에서는 전체 3차원 영상 처리 시스템중 거리 영상을 입력으로 했을 시 형상 특징을 추출하는 방법에 대해 제안하고자 한다. 이를 위해 첫째, 거리 영상의 깊이 변화 부호 값의 분포 특성에 따라 표면을 분류하는 방법을 제안하고자 한다. 또한 평균 곡률과 가우스 곡률을 이용하여 표면을 분류했던 기존 방법을 토대로 그의 문제점이었던 실제 거리 영상에서의 임계치 선정 방법에 대하여 제안하고자 한다. 끝으로 제안한 방법의 유용성을 실험에 의해 입증하고자 한다.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.663-676
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• 2020

Plant leaf classification is a significant application of image processing techniques in modern agriculture. In this paper, a multi-granular angle description method is proposed for plant leaf classification and retrieval. The proposed method can describe leaf information from coarse to fine using multi-granular angle features. In the proposed method, each leaf contour is partitioned first with equal arc length under different granularities. And then three kinds of angle features are derived under each granular partition of leaf contour: angle value, angle histogram, and angular ternary pattern. These multi-granular angle features can capture both local and globe information of the leaf contour, and make a comprehensive description. In leaf matching stage, the simple city block metric is used to compute the dissimilarity of each pair of leaf under different granularities. And the matching scores at different granularities are fused based on quotient space theory to obtain the final leaf similarity measurement. Plant leaf classification and retrieval experiments are conducted on two challenging leaf image databases: Swedish leaf database and Flavia leaf database. The experimental results and the comparison with state-of-the-art methods indicate that proposed method has promising classification and retrieval performance.

• Journal of Information Processing Systems
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• 제15권3호
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• pp.682-693
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• 2019

The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

• 대한기관식도과학회지
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• 제17권2호
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• pp.104-107
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• 2011

Background and Objectives Scope classification is designed to classify acute epiglottitis according to laryngoscopic findings. There is no report about the utility of classification; the difference between the diagnosis and the prognosis by the Scope classification was not found. The aim of this study was to evaluate the utility of Scope classification in patients with acute epiglottitis. Subject and Method 127 patients who had been admitted to our hospital were diagnosed with acute epiglottitis. The patients were classified by the Scope classification. We compared demographic characteristics, clinical symptoms and signs, laboratory findings, and clinical course among the patient groups and divided the results according to the Scope classification. Results There are no significant differences among the groups in demographic characteristics, clinical symptoms and signs, laboratory findings, and clinical course. Conclusion The Scope classification of acute epiglottitis does not seem to be a method to evaluate the severity of acute epiglottitis. Thus, we need to develop multidisciplinary approaches for acute epiglottitis.

• 대한지리학회지
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• 제43권5호
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• pp.761-774
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• 2008

원격탐사에서 위성 영상의 디지털 처리 기술이 발달하면서 GIS 자료와 지식 기반 전문가 시스템과의 통합에 대한 관심이 증가하고 있다. 본 연구에서는 위성영상을 토지피복 분류하는 과정에서 GIS 자료를 통합하기 위하여 기계 학습 기법과 규칙 기반 분류 기법을 적용하였다. 사례 지역을 대상으로 Landsat ETM+ 영상과 고도, 경사, 향, 수역과의 거리, 도로와의 거리, 인구밀도 등의 GIS 자료를 함께 활용하였다. C5.0 추론 기계 학습 알고리듬을 이용하여 350개의 표본점으로부터 결정 트리와 분류 규칙을 생성하였다. 본 연구에서 도출된 규칙을 이용하여 분류한 결과, 고독 수역과의 거리, 인구밀도 등의 GIS 자료가 규칙 기반 분류에 효과적인 것으로 나타났다. 본 연구에서 제안한 기계 학습과 지식 기반 분류 기법을 이용하면 다양한 GIS 자료들을 통합하여 위성영상을 보다 효과적으로 분류할 수 있다.

• 통합자연과학논문집
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• 제13권4호
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• pp.132-140
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• 2020

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.