• 한국정보통신학회논문지
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• 제19권1호
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• pp.35-40
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• 2015

본 논문은 유전 알고리즘 기반 귀납적 학습 환경 하에서 메타 학습법을 이용한 다중 분류기 시스템의 구축에 관한 것이다. 메타 학습법을 이용한 다중 분류기 시스템의 구축에서 분류기는 일반 분류기와 메타 분류기로 구성된다. 메타 분류기는 사례에 대한 일반 분류기의 분류 결과에 학습 알고리즘을 적용하여 얻어진다. 분류시스템의 의사 결정과정에서 메타 분류기의 역할은 일반 분류기의 분류 결과를 평가하여 최종 의사 결정 과정에의 참여 여부를 결정하는 것이다. 분류 시스템은 분류기의 분류 결과가 옳은 것으로 평가된 결과들만 취합하여 이를 바탕으로 최종 분류 결과를 도출해 낸다. 메타 학습법이 다중 분류기 시스템의 성능에 미치는 영향을 다수의 사례 집합을 이용하여 평가하였다.

• 한국지능시스템학회논문지
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• 제21권4호
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• pp.487-492
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• 2011

본 논문에서는 가장 단순한 구조를 가진 Nearest Prototype Classifier의 성능 개선을 위해 차분 진화 알고리즘을 적용하여 prototype의 위치를 결정하는 방법을 제안하였다. 차분 진화 알고리즘을 이용하여 prototype의 위치 벡터가 결정이 되며, 차분 진화 알고리즘에 의해 결정된 prototype의 class label을 결정하기 위한 class label 결정 알고리즘도 제안하였다. 제안된 알고리즘의 성능 평가를 위해 기존의 패턴 분류기와 비교 결과를 보인다.

• 한국지능시스템학회논문지
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• 제23권2호
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• pp.166-171
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• 2013

The method proposed in this paper can improve the performance of the Boosting algorithm in machine learning. The proposed Boundary AdaBoost algorithm can make up for the weak points of Normal binary classifier using threshold boundary concepts. The new proposed boundary can be located near the threshold of the binary classifier. The proposed algorithm improves classification in areas where Normal binary classifier is weak. Thus, the optimal boundary final classifier can decrease error rates classified with more reasonable features. Finally, this paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Boundary AdaBoost in a simulation experiment of pedestrian detection using 10-fold cross validation.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1999년도 가을 학술발표논문집 Vol.26 No.2 (2)
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• pp.551-553
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• 1999

This paper explores the utility of a new classifier fusion approach to discrimination. Multiple classifier fusion, a popular approach in the field of pattern recognition, uses estimates of each individual classifier's local accuracy on training data sets. In this paper we investigate the effectiveness of fusion methods compared to individual algorithms, including the artificial neural network and k-nearest neighbor techniques. Moreover, we propose an efficient meta-classifier architecture based on an approximation of the posterior Bayes probabilities for learning the oracle.

• 전자공학회논문지B
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• 제29B권7호
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• pp.23-29
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• 1992

In this paper, a method of representing the pattern classifier as a collection of hypercubic regions is proposed. This representation has following advantages over the conventional ones : 1) a simple form of human knowledge can be used in designing the classifier, 2) the form of the classifier is suit for the rule-based system, and 3) this can reduce the classification time. A method of synthesis of the classifier under this representation is also proposed and the experimental result shows that the proposed method is faster than the well-known nearest neighbor classifier.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

• 전기학회논문지
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• 제64권9호
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• pp.1337-1346
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• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권3호
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• pp.326-330
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• 2008

패턴인식 문제에 있어서 다수의 인식기를 사용하는 연구는 주로, 선택된 다수 인식기를 어떻게 결합할 것인가에 중점을 두어 왔으나, 최근에는 인식기 풀로부터 다수 인식기를 선택하려는 연구로 점차 진행되고 있다. 실제로 다수 인식기 시스템의 성능은 인식기들의 결합 방법은 물론, 선택되는 인식기에 의존한다. 따라서, 우수한 성능을 보이는 인식기 집합을 선택하는 것이 필요하며, 다수의 인식기를 선택하는데 있어서 정보이론에 기초한 접근 방법이 시도되었다. 본 논문에서는 인식기 간의 상호정보를 기반으로 인식기를 선택하여 인식기 집합을 구성하고, 다른 인식기 선택 방법들에 의해 구성된 인식기 집합과 그 성능을 비교해 보고자 한다.

• Journal of Information Processing Systems
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• 제14권5호
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• pp.1114-1135
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• 2018

A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual super-classifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.182-185
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• 2003

This paper presents a new design algorithm for the combination with the fuzzy classifier and the Bayesian classifier. Only few attempts have so far been made at providing an effective design algorithm combining the advantages and removing the disadvantages of two classifiers. Specifically, the suggested algorithms are composed of three steps: the combining, the fuzzy-set-based pruning, and the fuzzy set tuning. In the combining, the multi-inputs and multi-outputs (MIMO) fuzzy model is used to combine two classifiers. In the fuzzy-set-based pruning, to effectively decrease the complexity of the fuzzy-Bayesian classifier and the risk of the overfitting, the analysis method of the fuzzy set and the recursive pruning method are proposesd. In the fuzzy set tuning for the misclassified feature vectors, the premise parameters are adjusted by using the gradient decent algorithm. Finally, to show the feasibility and the validity of the proposed algorithm, a computer simulation is provided.