• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.293-304
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• 2001

frauds detection is a difficult problem, requiring huge computer resources and complicated search activities. researchers have struggled with the problem. Even though a flew research approaches have claimed that their solution is much bettor than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds, a Revel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims and credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new set of decision-making rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• Annual Conference of KIPS
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• 2019.10a
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• pp.864-867
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• 2019

본 연구에서는 러프 근사화 개념을 활용하여 섬망 환자를 사전에 구별할 수 있는 임상적 지식을 유도할 수 있는 방법을 제안하였다. 실험 결과에서는 평균 섬망기간이 13일 이상 유지되는 6가지 임상적 분류기준을 유도하였고, 통계적 5겹 교차검정 실험을 통해 73%의 분류 성능을 제공함을 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.109-112
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• 2006

본 논문에서는 PCA에 의한 특징추출과 k-NN과 SVM에 기반을 계층구조의 분류기에 의한 유도전동기의 고장진단 알고리즘을 제안한다. 제안된 방법은 k-NN에 의해 선형적으로 분류 가능한 고장패턴을 분류한 후, 분류가 되지 않는 부분을 커널 함수에 의해 고차원 공간으로 입력패턴을 매핑한 후 SVM에 의해 고장을 진단하는 계층구조를 갖는다. 실험장치를 구축한 후, 다양한 부하에 대하여 몇몇의 전기적 고장과 기계적 고장 하에서 획득한 데이터를 이용하여 제안된 방법의 타당성을 검증한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.167-170
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• 2006

본 논문은 선형분류기인 LDA 융합모델과 최소거리패턴분류법을 이용한 얼굴표정인식 알고리즘 연구에 관한 것이다. 제안된 알고리즘은 얼굴 표정을 인식하기 위해 두 단계의 특징 추출과정과 인식단계를 거치게 된다. 먼저 특징추출 단계에서는 얼굴 표정이 담긴 영상을 PCA를 이용해 고차원에서 저차원의 공간으로 변환한 후, LDA 이용해 특징벡터를 클래스 별로 나누어 분류한다. 다음 단계로 LDA융합모델을 통해 계산된 특징벡터에 최소거리패턴분류법을 적용함으로서 얼굴 표정을 인식한다. 제안된 알고리즘은 6가지 기본 감정(기쁨, 화남, 놀람, 공포, 슬픔, 혐오)으로 구성된 데이터베이스를 이용해 실험한 결과, 기존알고리즘에 비해 향상된 인식률과 특정 표정에 관계없이 고른 인식률을 보임을 확인하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.84-88
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• 2011

In this paper, a new competition strategy for learning vector quantization is proposed. The simple competitive strategy used for learning vector quantization moves the winning prototype which is the closest to the newly given data pattern. We propose a new learning strategy based on k-nearest neighbor prototypes as the winning prototypes. The selection of several prototypes as the winning prototypes guarantees that the updating process occurs more frequently. The design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the proposed learning strategy.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.124-128
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• 2011

Music emotion recognition is currently one of the most attractive research areas in music information retrieval. In order to use emotion as clues when searching for a particular music, several music based emotion recognizing systems are fundamentally utilized. In order to maximize user satisfaction, the recognition accuracy is very important. In this paper, we develop a new music emotion recognition system, which employs a multilabel feature selector and multilabel classifier. The performance of the proposed system is demonstrated using novel musical emotion data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.285-288
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• 2006

Most previous studies on improving the effectiveness of CBR have focused on the similarity function aspect or optimization of case features and their weights. However, according to some of the prior research, finding the optimal k parameter for the k-nearest neighbor (k-NN) is also crucial for improving the performance of the CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors that combine, as well as the weight of each feature. The new model is applied to the real-world case of a major telecommunication company in Korea in order to build the prediction model for the customer profitability level. Experimental results show that our GA-optimized CBR approach outperforms other AI techniques for this mulriclass classification problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.251-254
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• 2006

본 논문은 multiclass 문제에서 기존에 나와 있는 fuzzy support vector mahchines 이 decision boundary 를 설정하는데 있어 모든 훈련 데이터에 대해서 바람직한 decision boundary 를 만들지 못하므로 그러한 경우를 예로 제시한다. 그리고 그에 대한 개선점으로 밀도를 이용해 decision boundary 를 조정하여 기존 FSVM 의 decision boundary 보다 더 타당한 decision boundary 를 설정하는 것을 보인다.