• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.193-195
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• 2003

본 논문에서는 k-Nearest Neighbor(k-NN) 알고리즘을 최적화하기 위해 지역적으로 다른 k(고려할 neighbor의 개수)를 사용하는 새로운 방법을 제안한다. 인스턴스 공간(instance space)에서 노이즈(noise)의 분포가 지역적(local)으로 다를 경우, 각 지점에서 고려해야 할 최적의 이웃 인스턴스(neighbor)의 수는 해당 지점에서의 국부적인 노이즈 분포에 따라 다르다. 그러나 기존의 방법은 전체 인스턴스 공간에 대해 동일한 k를 사용하기 때문에 이러한 인스턴스 공간의 지역적인 특성을 고려하지 못한다. 따라서 본 논문에서는 지역적으로 분포가 다른 노이즈 문제를 해결하기 위해 인스턴스 공간을 여러 개의 부분으로 나누고, 각 부분에 최적화된 k의 값을 사용하여 kNN을 수행하는 새로운 방법인 Local-k Nearest Neighbor 알고리즘(LkNN Algorithm)을 제안한다. LkNN을 통해 생성된 k의 집합은 인스턴스 공간의 각 부분을 대표하는 값으로, 해당 지역의 인스턴스가 고려해야 할 이웃(neighbor)의 수를 결정지어준다. 제안한 알고리즘에 적합한 데이터의 도메인(domain)과 그것의 향상된 성능은 UCI ML Data Repository 데이터를 사용한 실험을 통해 검증하였다.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.2
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• pp.71-79
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• 1997

In this paper, we propose a new nearest-neighbor classifier with reduced computational complexity in search process. In the proposed classifier, the classes are divided into two sets: reference and non-reference sets. It reduces computational requriement by approximating the distance between the input and a class iwth the information of distances among the calsses. It calculates only the distance between the input and the reference classes. We convert a given classifier into RCC (reduced computational complexity but smal lincrease in misclassification probability of its corresponding RCC classifier. We designed RCC classifiers for the recognition of digits from the NIST database. We obtained an RCC classifier with 60% reduction in the computational complexity with the cost of 0.5% increase in misclassification probability.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.852-858
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• 1991

An adaptive hierarchical classifier(AHCL) for Korean character recognition using a neural net is designed. This classifier has two neural nets: USACL (Unsupervised Adaptive Classifier) and SACL (Supervised Adaptive Classifier). USACL has the input layer and the output layer. The input layer and the output layer are fully connected. The nodes in the output layer are generated by the unsupervised and nearest neighbor learning rule during learning. SACL has the input layer, the hidden layer and the output layer. The input layer and the hidden layer arefully connected, and the hidden layer and the output layer are partially connected. The nodes in the SACL are generated by the supervised and nearest neighbor learning rule during learning. USACL has pre-attentive effect, which perform partial search instead of full search during SACL classification to enhance processing speed. The input of USACL and SACL is a directional edge feature with a directional receptive field. In order to test the performance of the AHCL, various multi-font printed Hangul characters are used in learning and testing, and its processing its speed and and classification rate are compared with the conventional LVQ(Learning Vector Quantizer) which has the nearest neighbor learning rule.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.88-96
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• 2007

This paper discusses the fast modulation classifiers capable of classifying both analog and digital modulation signals in wireless communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulated signals. In this paper, we investigate the performance of the two types of fast modulation classifiers (i.e. 2 nearest neighbor classifiers and 2 minimum distance classifiers) and compare the performance of these classifiers with that of the state of the art for the existing classification methods such as SVM Classifier. Computer simulations indicate good performance on an AWGN channel, even at low signal-to-noise ratios, in case of minimum distance classifiers (MDC for short) and k nearest neighbor classifiers (kNNC for short). Besides a good performance, these type classifiers are considered as ideal candidate to adapt real-time software radio because of their fast modulation classification capability.

• 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 Korean Society for Precision Engineering
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• v.23 no.8 s.185
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• pp.80-88
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• 2006

This paper proposes a new steel strip surface inspection system. The system acquires bright and dark field images of defects by using a stroboscopic IR LED illuminator and area camera system and the defect images are preprocessed and segmented in real time for feature extraction. 4113 defect samples of hot rolled steel strip are used to develop KNN (k- Nearest Neighbor) classifier which classifies the defects into 8 different types. The developed KNN classifier demonstrates about 85% classifying performance which is considered very plausible result.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.379-383
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• 2005

This paper proposes a new steel strip surface inspection system. The system acquires bright and dark field images of defects by using a stroboscopic IR LED light and area camera system and the defect images are preprocessed and segmented in real time for feature extraction. 4113 defect samples of cold roll steel strips are used to develop KNN (k-Nearest Neighbor) classifier which classifies the defects into 8 different types. The developed KNN classifier demonstrates about 85% classifying performance which is considered very plausible result.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.11-17
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• 2017

We proposed malware detection method, which use the feature vector that consist of Opcode(operation code) and Windows API Calls extracted from executable files. And, we implemented our feature vector and measured the performance of it by using Bernoulli Naïve Bayes and K-Nearest Neighbor classifier. In experimental result, when using the K-NN classifier with the proposed method, we obtain 95.21% malware detection accuracy. It was better than existing methods using only either Opcode or Windows API Calls.