• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.73-81
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• 2015

The paper proposes a prototype selection method and evaluates the generalization performance of standard algorithms and prototype based classification learning. The proposed prototype classifier defines multidimensional spheres with variable radii within class areas and generates a small set of training data. The nearest-neighbor classifier uses the new training set for predicting the class of test data. By decomposing bias and variance of the mean expected error value, we compare the generalization errors of k-nearest neighbor, Bayesian classifier, prototype selection using fixed radius and the proposed prototype selection method. In experiments, the bias-variance changing trends of the proposed prototype classifier are similar to those of nearest neighbor classifiers with all training data and the prototype selection rates are under 27.0% on average.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.17-27
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• 2010

Feature selection and feature weighting are useful techniques for improving the classification accuracy of k-Nearest Neighbor (k-NN) classifier. The main propose of feature selection and feature weighting is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. In this paper, a novel hybrid approach is proposed for simultaneous feature selection, feature weighting and choice of k in k-NN classifier based on Genetic Algorithm. The results have indicated that the proposed algorithm is quite comparable with and superior to existing classifiers with or without feature selection and feature weighting capability.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.742-745
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• 2017

본 논문은 심장질환 도메인에 데이터 마이닝 기법을 적용한 연구로, 기존 환자의 정보에 대하여 K-최근접 이웃 알고리즘을 통해 결측 값을 대체하고, 대표적인 예측 분류기인 나이브 베이지안, 소포트 벡터 머신, 그리고 다층 퍼셉트론을 적용하여 각각 결과를 비교 및 분석한다. 본 연구의 실험은 K 최적화 과정을 포함하고 10-겹 교차 검증 방식으로 수행되었으며, 비교 및 분석은 정확도와 카파 통계치를 통해 판별한다.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.711-718
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• 2011

The k-Nearest Neighbor classifier that does not require a training phase is appropriate for a variable number of classes problem like face recognition, Recently distance metric learning methods that is trained with a given data set have reported the significant improvement of the kNN classifier. However, the performance of a distance metric learning method is variable for each application, In this paper, we focus on the face recognition and compare the performance of the state-of-the-art distance metric learning methods, Our experimental results on the public face databases demonstrate that the Mahalanobis distance metric based on PCA is still competitive with respect to both performance and time complexity in face recognition.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.279-286
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• 2006

Classification method used in this paper is applied for the first time to Korean traditional music. Among the frequency distribution vectors, average peak value is suggested and proved effective comparing to previous classification success rate. Mean, variance, spectral centroid, average peak value and ZCR are used to classify Korean traditional musical instruments. To achieve Korean traditional instruments automatic classification, Spectral analysis is used. For the spectral domain, Various functions are introduced to extract features from the data files. k-NN classification algorithm is applied to experiments. Taegum, gayagum and violin are classified in accuracy of 94.44% which is higher than previous success rate 87%.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.21-28
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• 2015

In this paper, we propose a prototype-based classification learning by using the nearest-neighbor rule. The nearest-neighbor is applied to segment the class area of all the training data into spheres within which the data exist from the same class. Prototypes are the center of spheres and their radii are computed by the mid-point of the two distances to the farthest same class point and the nearest another class point. And we transform the prototype selection problem into a set covering problem in order to determine the smallest set of prototypes that include all the training data. The proposed prototype selection method is based on a greedy algorithm that is applicable to the training data per class. The complexity of the proposed method is not complicated and the possibility of its parallel implementation is high. The prototype-based classification learning takes up the set of prototypes and predicts the class of test data by the nearest neighbor rule. In experiments, the generalization performance of our prototype classifier is superior to those of the nearest neighbor, Bayes classifier, and another prototype classifier.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.3
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• pp.52-58
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• 2002

The purpose of condition monitoring and fault diagnosis is to detect faults occurring in machinery in order to improve the level of safety in plants and reduce operational and maintenance costs. The recognition performance is important not only to gain a high recognition rate bur a1so to minimize the diagnosis failures error rate by using off effective rejection module. We examined the problem of performance evaluation for the rejection scheme considering the accuracy of individual c1asses in order to increase the recognition performance. We use the Smith's method among the previous studies related to rejection method. Nearest neighbor classifier is used for classifying the machine conditions from the vibration signals. The experiment results for the performance evaluation of rejection show the modified optimum rejection method is superior to others.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.27-32
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• 2005

Recently there has been much interest in applying data mining to computer network intrusion detection. A new approach, based on the k-Nearest Neighbour(kNN) classifier, is used to classify Program behaviour as normal or intrusive. Each system call is treated as a word and the collection of system calls over each program execution as a document. These documents are then classified using kNN classifier, a Popular method in text mining. A simple example illustrates the proposed procedure.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.295-297
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• 2001

최근 생물 유전자 정보를 효과적으로 분석하기 위한 적절한 도구의 필요성이 대두되고 있다. 본 논문에서는 백혈병 환자의 골수로부터 얻어낸 DNA Microarray 유전 정보를 분류하여 환자가 가지고 있는 암의 종류를 예측하기 위한 최적의 특징추출방법과 분류 방법을 찾고자 한다. 이를 위해 피어슨 상관관계, 유클리디안 거리, 코사인 계수, 스피어맨 상관관계, 정보 이득, 상호 정보, 신호 대잡음비의 7가지 특징 추출 방법을 사용하였으며, 역전과 신경망, 의사결정 트리, 구조 적응형 자기구성 지도, $textsc{k}$-최근접 이웃 등 가지의 기계학습 분류기를 이용하여 분류 실험을 하였다. 실험결과, 피어슨 상관관계와 역전파 신경망을 이용한 분류 방법이 97.1%의 인식률을 보임을 알 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.298-300
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• 2001

본 논문에서는 구조적응 자기구성 지도 모델을 사용하여 인간 행동의 성별을 분류하는 인식기를 제안하였다. 26명의 사람이 '화난 상태' 혹은 '보통 상태'의 두가지 정서 하에서 '문 두드리기', '손 흔들기', '물건 들어올리기'의 세가지 동작을 수행하는 동안, 행위자 관절점의 속도나 위치 정보로부터 성별을 분류하였다. 또한 SASOM의 성능 비교 분석을 위하여 전통적인 SOM, 다층 퍼셉트론과 거의 두 가지 결합 모델, SASOM와 의사결정트리 결합 모델, 단일 의사 결정트리, $textsc{k}$-최근접 이웃 등의 인식기를 구현하여 성능을 비교분석 하였다. 실험 결과 SASOM 분류기가 가장 높은 이식률을 보였으며 분류기로서 유용함을 알 수 있었다.