• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.145-154
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• 2024

In order to operate predictive models utilizing administrative big data, it is crucial to consider policy changes and the characteristics of highly volatile data. Considering this scenario, this study proposes the Cut-off Voting Classifier (CVC) algorithm. This proposed algorithm prevents a sharp decline in accuracy by utilizing multiple weak classifiers. The study validates the proposed algorithm's performance through experiments. The performance evaluation demonstrates the ability to maintain stable prediction rates even in situations with a sharp decline in predictive model accuracy.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.525-536
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• 2004

Gene expression profile is numerical data of gene expression level from organism, measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify disease with gene expression profile. Because all genes are not related to disease, it is needed to select related genes that is called feature selection, and it is needed to classify selected genes properly. This paper Proposes GA based method for searching optimal ensemble of feature-classifier pairs that are composed with seven feature selection methods based on correlation, similarity, and information theory, and six representative classifiers. In experimental results with leave-one-out cross validation on two gene expression Profiles related to cancers, we can find ensembles that produce much superior to all individual feature-classifier fairs for Lymphoma dataset and Colon dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.591-594
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• 2020

불균형 데이터는 범주에 따른 데이터의 분포가 불균형한 데이터를 의미한다. 이런 데이터를 활용해 기존 분류 알고리즘으로 분류기를 학습하면 성능이 저하되는 문제가 발생한다. 오버 샘플링은 이를 해결하기 위한 기법 중 하나로 수가 적은 범주[이하 소수 범주]에 속한 데이터 수를 임의로 증가시킨다. 기존 연구들에서는 수가 많은 범주[이하 다수 범주]에 속한 데이터 수와 동일한 크기만큼 증가시키는 경우가 많다. 이는 증가시키는 샘플의 수를 결정할 때 범주 간 데이터 수 비율만 고려한 것이다. 그런데 데이터가 동일한 수준의 불균형 정도를 갖더라도 범주별 데이터 분포에 따라서 분류 복잡도가 다르며, 경우에 따라 데이터 분포에서 존재하는 불균형 정도를 완전히 해소하지 않아도 된다. 이에 본 논문은 분류 복잡도를 활용해 데이터 셋 별 적정 오버 샘플링 비율을 산출하는 알고리즘을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.665-670
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• 2014

A SVM is a kind of binary classifier in order to find optimal hyperplane which separates training data into two groups. Due to its remarkable performance, the SVM is applied in various fields such as inductive inference, binary classification or making predictions. Also it is a representative black box model; there are plenty of actively discussed researches about analyzing trained SVM classifier. This paper conducts a study on a method that is automatically detecting the line-shaped echoes, sun strobe echo and radial interference echo, using the SVM algorithm because the line-shaped echoes appear relatively often and disturb weather forecasting process. Using a spatial clustering method and corrected reflectivity data in the weather radar, the training data is made up with mean reflectivity, size, appearance, centroid altitude and so forth. With actual occurrence cases of the line-shaped echoes, the trained SVM classifier is verified, and analyzed its characteristics using the decision tree method.

• Journal of Korean Society of Transportation
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• v.17 no.4
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• pp.111-124
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• 1999

The Purpose of this thesis is to develop a vehicle classification algorithm using single Magnetometer detector during presence time of vehicle detection and is to examine a held application from field test. We collected data using Magnetometer detector on freeway and used digital data to change voltage values according to magnetic flux density in analysis. We collected these datum during the presence time and then obtained characteristics from wave form in these datum. Based on these characteristics, We used the following three methods for this a1gorithm :1. Template Matching Method,2. Neural Network Method using Back-propagation Algorithm 3. Complex Method using changed slope points and mixing method 1, 2. Of course, Before processing of over three methods, These data were processed normalizing by 20, 40 of size in only X axis and moving average by 0, 3, 4, 5 of size. Vehicle classification were Processed in three steps ; 2, 3, 5 types classification. In 2 types vehicle classification, recognition rate is 83% by template matching method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.177-180
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• 2019

To improve the accuracy rate of guided weapons, many studies have been conducted on the accurate detection and identification of targets from sea clutter. Because of the variety and complicated characteristics of both sea-clutter and target signals, an active target recognition technique is required. In this study, we propose an algorithm to distinguish clutter and recognize targets by applying a fractal signature(FS) classifier, which is a fractal dimension, and a high-resolution target image(HRTI) classifier, which applies scene matching to an image formed from a scanned image. Simulation results using the algorithm revealed that the HRTI classifier recognized targets 1 and 2 at a 100 % rate, whereas the FS classifier recognized targets 1 and 2 at rates of 90 % and 93 %, respectively.

• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.425-435
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• 2002

This paper introduces a macroblock-based rate control algorithm using the neural classifier based in Self Organization feature Maps (SOFM). In contrast to the conventional rate control methods based on the mathematical rate distortion (RD) model and the feedback regression, proposed method can actively adapt to the rapid-varying image characteristics by establishing the global model for bitrate control and by using the SOFM based neural classifier to manage that model. Proposed rate control algorithm has 0.2 dB ~ 0.6 dB better performances than MPEG-4 macroblock-based rate control algorithm by evaluating with the encoded Peak Signal to Noise Ratios while maintaining similar overall computational complexity.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1204-1217
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• 2004

Numerous statistical and machine learning techniques have been studied for automatic text classification. However, because they train the classifiers using only feature vectors of documents, ambiguity between two possible categories significantly degrades precision of classification. To remedy the drawback, we propose a new method which incorporates relationship information of categories into extant classifiers. In this paper, we first perform the document classification using the k-NN classifier which is generally known for relatively good performance in spite of its simplicity. We employ the relationship information from an object-based thesaurus to reduce the ambiguity. By referencing various relationships in the thesaurus corresponding to the structured categories, the precision of k-NN classification is drastically improved, removing the ambiguity. Experiment result shows that this method achieves the precision up to 13.86% over the k-NN classification, preserving its recall.

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.160-162
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• 2013

편리한 블로그 사용과 블로그에서의 정보 탐색을 위해서는 내용에 기반한 분류가 필요하다. 대부분의 블로그 사이트에서는 내용 기반 분류를 제공하고 있으나, 블로거들은 자신이 작성한 블로그에 대한 수동 분류를 입력하지 않는 경우가 많다. 본 논문에서는 분류가 제공되는 블로그 사이트에서 각 분류별 문서를 수집하고, 어휘빈도와 문서빈도, 분류별 빈도를 활용하여 문서 내 어휘의 자질 가중치를 부여하고, 다양한 학습기를 이용하여 분류 모델을 생성한 뒤 블로그의 특성에 적합한 자질 추출 알고리즘과 분류 알고리즘을 찾아낸다. 실험에서는 본 논문에서 고안한 CTF-IECDF와 나이브 베이즈 멀티노미얼로 조합한 분류 모델이 75.40%의 분류 정확률을 보였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.199-202
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• 2005

TV OSD(On Screen Display) 메뉴 자동검증 시스템에서 다국적 언어의 문자 인식은 표준패턴의 구조적 분석이 쉽지 않을 뿐만 아니라 학습패턴 집합의 규모와 특징의 수가 증가함으로 인하여 특징추출 및 인식 과정에서 방대한 계산량이 요구된다. 이에 본 연구에서는 학습 데이터에 포함되는 다량의 특징 집합으로부터 인식에 필요한 효과적인 특징을 선별함으로써 패턴 분류기의 효율성을 개선하기 위한 방법론을 고찰한다. 이를 위하여 수정된 형태의 Adaboost 기법을 제안하고 이를 적용한 실험 결과로부터 그 유용성을 고찰한다. 제안된 알고리즘은 초기의 특징 집합을 취약한 성능을 갖는 다수의 분류기(classifier)로서 고려하며, 이로부터 반복학습을 통하여 개선된 분류기를 점진적으로 선별해 나가게 된다. 학습의 원리는 주어진 학습패턴 집합에 기초하여 일종의 교사학습(supervised learning) 방식으로 이루어진다. 각 패턴에 할당된 가중치 값은 각 단계에서 산출되는 분류결과에 따라 적응적으로 수정되어 반복학습이 진행됨에 따라 점차 보완적 성능을 갖는 분류기를 선택할 수 있게 한다. 즉, 주어진 각 학습패턴에 대하여 초기에 균등한 가중치가 부여되며, 반복학습의 각 단계에서 적용되는 분류기의 출력을 분석하여 오분류된 패턴의 가중치 분포를 증가시켜 나간다. 본 연구에서는 실제 응용으로서 OSD 메뉴검증 시스템을 대상으로 제안된 이론을 적용하고 그 타당성을 평가한다.