• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1998년도 Proceedings of International Symposium on Remote Sensing
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• pp.122-127
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• 1998

This paper presents the neuro-fuzzy classifier derived from the generic model of a 3-layer fuzzy perceptron and developed the classification software based on the neuro-fuzzl model. Also, a comparison of the neuro-fuzzy and maximum-likelihood classifiers is presented in this paper. The Airborne Multispectral Scanner(AMS) imagery of Tae-Duk Science Complex Town were used for this comparison. The neuro-fuzzy classifier was more considerably accurate in the mixed composition area like "bare soil" , "dried grass" and "coniferous tree", however, the "cement road" and "asphalt road" classified more correctly with the maximum-likelihood classifier than the neuro-fuzzy classifier. Thus, the neuro-fuzzy model can be used to classify the mixed composition area like the natural environment of korea peninsula. From this research we conclude that the neuro-fuzzy classifier was superior in suppression of mixed pixel classification errors, and more robust to training site heterogeneity and the use of class labels for land use that are mixtures of land cover signatures.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 춘계 학술발표회
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• pp.999-1008
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• 2009

A series of sedimentary rocks which are formed in the Tertiary are distributed around Samcheok(Samcheok-Pukpyoung basin), Younghae(Younghae basin), Pohang(Pohang basin), Gyeongju(Yangnam basin), Ulsan(Ulsan basin), Jeju(Seogyuipo formation) in the southern region of the Korean Peninsula. This study concerned with geological, geophysical, geotechnical properties of the unconsolidated rocks in the Pohang area. A consolidated rocks are classified as hard rock - soft rock - weathered rock - residual soil follows in degree of weathering. But unconsolidated rocks has soil properties as well as rock's at the same time. The results of field excursion, boring, borehole-logging, rock testing, geophysical survey, laboratory test are soft rock range, but the durability of the rock until the residual soil from the weathered rock. We accomplished the rock mass classification of the unconsolidated rocks.

• International Journal of Knowledge Content Development & Technology
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• 제7권1호
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• pp.101-120
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• 2017

Healthcare professionals have edited many health questions (HQs) and their answers for healthcare consumers on the Internet. The HQs provide both readable and reliable health information, and hence retrieval of those HQs that are relevant to a given question is essential for health education and promotion through the Internet. However, retrieval of relevant HQs needs to be based on the recognition of the intention of each HQ, which is difficult to be done by predefining syntactic and semantic rules. We thus model the intention recognition problem as a text classification problem, and develop two techniques to improve a learning-based text classifier for the problem. The two techniques improve the classifier by location-based and area-based feature weightings, respectively. Experimental results show that, the two techniques can work together to significantly improve a Support Vector Machine classifier in both the recognition of HQ intentions and the retrieval of relevant HQs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.263-267
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• 2005

Building classifiers for financial real-world classification problems is often plagued by severely overlapping and highly skewed class distribution. New performance measures such as receiver operating characteristic (ROC) curve and area under ROC curve (AUC) have been recently introduced in evaluating and building classifiers for those kind of problems. They are, however, in-effective to evaluation of classifier's discrimination performance in a particular class of the classification problems that interests lie in only a local operating range of the classifier, In this paper, a new method is proposed that enables us to directly improve classifier's discrimination performance at a desired local operating range by defining and optimizing a partial area under ROC curve or domain-specific curve, which is difficult to achieve with conventional classification accuracy based learning methods. The effectiveness of the proposed approach is demonstrated in terms of fraud detection capability in a real-world fraud detection problem compared with the MSE-based approach.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.624-627
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• 2005

This paper presents a new data classification method using the Robbins Monro stochastic approximation algorithm k-nearest neighbor and distribution analysis. To cluster the data set, we decide the centroid of the test data set using k-nearest neighbor algorithm and the local area of data set. To decide each class of the data, the Robbins Monro stochastic approximation algorithm is applied to the decided local area of the data set. To evaluate the performance, the proposed classification method is compared to the conventional fuzzy c-mean method and k-nn algorithm. The simulation results show that the proposed method is more accurate than fuzzy c-mean method, k-nn algorithm and discriminant analysis algorithm.

• Water Engineering Research
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• 제2권1호
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• pp.33-48
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• 2001

For the design of suitable aquatic habitats and habitat management purposes, sensitive descriptors for aquatic areas were identified and analyzed. The classification system of the aquatic areas were developed for natural streams and modified streams in Korea. Relationships among the descriptors of an aquatic area such as channel width, meander wave length, and arc angle have been defined. The analysis indicates that the total mean sinuosity is 1.25 for the main channels of natural streams, whereas the mean value of the sinuosity of modified streams is 1.14. The mean values of the total area, the width, and the length for the sandbars of natural streams are larger than those of modified streams.

• 한국지리정보학회지
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• 제6권2호
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• pp.80-91
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• 2003

본 연구에서는 밴드간 연산데이터를 재사용하는 방법이 위성영상를 이용한 토지피복 분류시의 정확도를 향상시키는 방법으로 제안되고 검증되었다. 분류항목별로 연산에 사용할 밴드를 결정하기 위해 밴드 6을 제외한 6개의 밴드를 조합이 가능한 수로 조합하고 감독분류의 최대우도법으로 토지피복 분류를 실시하였다. 49가지로 조합된 밴드의 토지피복 분류결과에서, 정확도가 상위 10위내에 분류된 밴드조합에서 사용된 횟수가 많은 두 밴드를 선정하고 연산하였다. 여기서 얻어진 연산결과를 재구성하여 다시 토지피복 분류를 실시하였다. 그리고 원 데이터를 사용한 토지피복 분류결과와 비교, 검토하였다. 연산 결과를 재구성한 데이터와 원 데이터를 사용한 토지피복 분류를 비교 검토한 결과, 연산결과를 재구성하여 사용한 토지피복 분류에서 나지에 대한 정확도가 조금 떨어진 반면 전체적으로 정확도가 향상됨을 알 수 있었다. 특히 인공지물에 대한 정확도가 향상되었기 때문에 이후 도시역에 대한 토지피복 분류 및 지표정보를 분석할 때 밴드간 연산데이터를 재 사용하는 방법이 유용할 것으로 판단된다.

• International Journal of Contents
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• 제13권2호
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• pp.66-74
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• 2017

Patent classification is becoming more critical as patent filings have been increasing over the years. Despite comprehensive studies in the area, there remain several issues in classifying patents on IPC hierarchical levels. Not only structural complexity but also shortage of patents in the lower level of the hierarchy causes the decline in classification performance. Therefore, we propose a new method of classification based on different criteria that are categories defined by the domain's experts mentioned in trend analysis reports, i.e. Patent Landscape Report (PLR). Several experiments were conducted with the purpose of identifying type of features and weighting methods that lead to the best classification performance using Support Vector Machine (SVM). Two types of features (noun and noun phrases) and five different weighting schemes (TF-idf, TF-rf, TF-icf, TF-icf-based, and TF-idcef-based) were experimented on.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.555-559
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• 1998

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis have been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, We proposed an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted four morphological features parameters such as centromeric index (C.I.), relative length ratio(R.L.), relative area ratio(R.A.) and chromosome length(C.L.). These Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results shown that the chromosome classification error was reduced much more than that of the other classification methods.

• 대한산업공학회지
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• 제41권5호
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• pp.439-446
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• 2015

Recently, the importance of one-class classification problem is more increasing. However, most of existing algorithms have the limitation on providing the information that effects on the prediction of the target value. Motivated by this remark, in this paper, we suggest an efficient one-class classifier using hyper-rectangles (H-RTGLs) that can be produced from intervals including observations. Specifically, we generate intervals for each feature and integrate them. For generating intervals, we consider two approaches : (i) interval merging and (ii) clustering. We evaluate the performance of the suggested methods by computing classification accuracy using area under the roc curve and compare them with other one-class classification algorithms using four datasets from UCI repository. Since H-RTGLs constructed for a given data set enable classification factors to be visible, we can discern which features effect on the classification result and extract patterns that a data set originally has.