• 한국멀티미디어학회논문지
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• 제20권9호
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• pp.1567-1573
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• 2017

The k-nearest neighbor (k-NN) algorithm is one of the most widely used benchmark algorithm in classification. Nowadays it has been further applied to predict time series. However, one of the main concerns of the algorithm applied on short-term electricity load forecasting is high computational burden. In the paper, we propose an approach of dimension reduction that follows the principles of highlighting the temperature effect on electricity load data series. The results show the proposed approach is able to reduce the dimension of the data around 30%. Moreover, with temperature effect highlighting, the approach will contribute to finding similar days accurately, and then raise forecasting accuracy slightly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.3896-3915
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• 2014

The Reverse k Nearest Neighbor (RkNN) query is valuable for finding objects influenced by a specific object and is widely used in both scientific and commercial systems. However, the influence level of each object is unknown, information that is critical for some applications (e.g. target marketing). In this paper, we propose a new query type, Ordered Reverse k Nearest Neighbor (ORkNN), and make efforts to adapt it in an on-demand scenario. An Order-k Voronoi diagram based approach is used to answer ORkNN queries. In particular, for different values of k, we pre-construct only one Voronoi diagram. Algorithms on both the server and the clients are presented. We also present experimental results that suggest our proposed algorithms may have practical applications.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권4호
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• pp.273-282
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• 2009

소프트웨어 프로젝트 데이터를 이용한 각종 분석 예측 모델 생성시 직면하는 문제 중 하나는 데이터에 포함된 결측값이며 이에 대한 효과적인 방안은 결측값 대치 법이다. 대표적인 결측값 대치법인 K 최근접 이웃 대치법은 대치과정에서 결측값을 포함하는 인스턴스의 관측정보를 활용하지 못한다는 단점이 있다. 본 연구에서는 이러한 단점을 극복하기 위해 K 최근접 이웃 대치법과 최대 우도 추정법을 결합한 새로운 소프트웨어 프로젝트 수치 데이터용 결측값 대치법을 제안한다. 또한 결측값 대치법의 정확도를 비교하기 위한 새로운 측도를 함께 제안한다.

• 한국정보과학회논문지:데이타베이스
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• 제35권5호
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• pp.447-458
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• 2008

본 논문에서는 도로 네트워크 데이타베이스에서 정적 객체의 k-최근접 이웃 질의를 효율적으로 처리하기 위한 방안을 논의한다. 기존의 여러 기법들은 인덱스를 사용하지 못했는데, 이는 네트워크 거리가 순서화 된 거리함수가 아니며 삼각 부등식(triangular inequality) 성질 또한 만족하지 못하기 때문이다. 이러한 기존 기법들은 질의 처리 시 심각한 성능 저하의 문제를 가진다. 선계산된 네트워크 거리를 이용하는 또 다른 기법은 저장 공간의 오버헤드가 크다는 문제를 갖는다. 본 논문에서는 이러한 두 가지 문제점들을 동시에 해결하기 위하여 객체들 간의 네트워크 거리를 근사하여 객체들에 대한 인덱스를 구축하고, 이를 이용하여 k-최근접 이웃 질의를 처리하는 새로운 기법을 제안한다. 이를 위하여 본 논문에서는 먼저 네트워크 공간상의 객체를 유클리드 공간상으로 사상하기 위한 체계적인 방법을 제시한다. 특히, 삼각 부등식 성질을 만족시키기 위하여 평균 네트워크 거리라는 새로운 거리 개념을 제시하고, 유클리드 공간으로의 사상을 위하여 FastMap 기법을 사용한다. 다음으로, 평균 네트워크 거리와 FastMap을 사용하여 네트워크 공간상의 객체들로 인덱스를 구축하는 근사 색인 알고리즘을 제시한다. 또한, 구축한 인덱스를 사용하여 k-최근접 이웃 질의를 효과적으로 수행하는 알고리즘을 제안한다. 마지막으로, 실제 도로 네트워크를 이용한 다양한 실험을 통하여 제안된 기법의 우수성을 규명한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권2호
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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.

• Journal of Multimedia Information System
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• 제5권2호
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• pp.67-78
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• 2018

This article proposes the modified KNN (K Nearest Neighbor) algorithm which considers the feature similarity and is applied to the word categorization. The texts which are given as features for encoding words into numerical vectors are semantic related entities, rather than independent ones, and the synergy effect between the word categorization and the text categorization is expected by combining both of them with each other. In this research, we define the similarity metric between two vectors, including the feature similarity, modify the KNN algorithm by replacing the exiting similarity metric by the proposed one, and apply it to the word categorization. The proposed KNN is empirically validated as the better approach in categorizing words in news articles and opinions. The significance of this research is to improve the classification performance by utilizing the feature similarities.

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.977-991
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• 2008

본 논문에서는 연속형 변수에 대한 결합확률분포를 추정하지 않고도 상호정보(MI) 추정량을 구할 수 있는 k-최근접이웃 기반방법에 대하여 연구하였다. 변수가 동일한 값들을 가지는 경우 k-최근접이웃을 구할 때 생기는 문제점을 해결하기 위하여 지터링(jittering)과 붓스트랩(bootstrap) 방법을 제안하였다. 몬테칼로 모의실험과 실제 데이터에 대한 실험을 수행한 결과, k=1과 같이 작은 값을 사용한 k-최근접이웃 기반방법에 의해 효율적인 MI 추정량을 구할 수 있었다. k-최근접이웃 기반방법은 연속형 설명변수, 범주형 또는 연속형인 목적변수 형태의 데이터에 적용할 수 있으며, 목적변수에 영향을 주는 중요한 설명변수의 순서를 구할 수 있을 뿐만 아니라 다차원에도 적용할 수 있기 때문에 중요변수의 집합을 구하는 변수 선택(feature subset selection) 문제에도 적용할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권2호
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• pp.742-762
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• 2015

In this study, we propose a distance metric learning approach called discriminant metric learning (DML) for face verification, which addresses a binary-class problem for classifying whether or not two input images are of the same subject. The critical issue for solving this problem is determining the method to be used for measuring the distance between two images. Among various methods, the large margin nearest neighbor (LMNN) method is a state-of-the-art algorithm. However, to compensate the LMNN's entangled data distribution due to high levels of appearance variations in unconstrained environments, DML's goal is to penalize violations of the negative pair distance relationship, i.e., the images with different labels, while being integrated with LMNN to model the distance relation between positive pairs, i.e., the images with the same label. The likelihoods of the input images, estimated using DML and LMNN metrics, are then weighted and combined for further analysis. Additionally, rather than using the k-nearest neighbor (k-NN) classification mechanism, we propose a verification mechanism that measures the correlation of the class label distribution of neighbors to reduce the false negative rate of positive pairs. From the experimental results, we see that DML can modify the relation of negative pairs in the original LMNN space and compensate for LMNN's performance on faces with large variances, such as pose and expression.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2008년도 공동추계학술대회
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• pp.297-302
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• 2008

The positioning technology for a moving object is an important and essential component of ubiquitous computing environment and applications, for which Radio Frequency Identification(RFID) has been considered as a core technology. RFID-based positioning system calculates the position of moving object based on k-nearest neighbor(k-nn) algorithm using detected k-tags which have known coordinates and kcan be determined according to the detection range of RFID system. In this paper, RFID-based positioning system determines the position of moving object not using weight factor which depends on received signal strength but assuming that tags within the detection range always operate and have same weight value. Because the latter system is much more economical than the former one. The geometries of tags were determined with considerations in huge buildings like office buildings, shopping malls and warehouses, so they were determined as the line in I-Dimensional space, the square in 2-Dimensional space. In 1-Dimensional space, the optimal detection range is determined as 125% of the tag spacing distance through the analytical and numerical approach. Here, the analytical approach means a mathematical proof and the numerical approach means a simulation using matlab. But the analytical approach is very difficult in 2-Dimensional space, so through the numerical approach, the optimal detection range is determined as 134% of the tag spacing distance in 2-Dimensional space. This result can be used as a fundamental study for designing RFID-based positioning system.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1872-1879
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• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.