• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.82.1-82
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• 2002

$\textbullet$ Nearest neighbor filter $\textbullet$ Probabilistic nearest neighbor filter $\textbullet$ Probabilistic nearest neighbor filter incorporating numbers of validated measurements $\textbullet$ Probability density function of the NDS $\textbullet$ Simulation results in a clutter environment to verify the performances $\textbullet$ Sensitivity analysis for the unknown spatial clutter density

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.872-878
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• 2000

In this paper a computer programming for the expression of nearest neighbor atoms in face-centered cubic (FCC) and body-centered cubic (BCC) crystals was suggested as one of the approaches to understand each of the crystal structure. By using this computer programming the distance values between a reference atom and the nearest neighbor atoms, and the numbers of the nearest neighbor atoms were calculated ane compared for the FCC and BCC crystals. In this algorithm, the positions of the atoms in a crystal were defined as two categories: the corner atoms and face- or body-centered atoms, and considered respectively. For the same order of nearest neighbor atoms except the second order ones the distance values form the reference atom were smaller in the FCC crystals than those in the BCC. Also, the numbers of he first and third nearest neighbor atoms n the FCC crystals were larger than those in the BCC. This difference was explained by the comparison of each atomic packing ratio of the FCC and BCC crystals. The algorithm used in this programming can also be expanded to the analysis of other crystal structures.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.401-410
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• 2021

Popular in discriminant classification analysis, k-nearest neighbor classification methods have limitations that do not reflect the local characteristic of the data, considering only the number of fixed neighbors. Considering the local structure of the data, the adaptive nearest neighbor method has been developed to select the number of neighbors. In the analysis of high-dimensional data, it is common to perform dimension reduction such as random projection techniques before using k-nearest neighbor classification. Recently, an ensemble technique has been developed that carefully combines the results of such random classifiers and makes final assignments by voting. In this paper, we propose a novel discriminant classification technique that combines adaptive nearest neighbor methods with random projection ensemble techniques for analysis on high-dimensional data. Through simulation and real-world data analyses, we confirm that the proposed method outperforms in terms of classification accuracy compared to the previously developed methods.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.83-90
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• 2010

The number of searched nodes and the computation time in an index should be minimized for optimizing the processing cost of the nearest neighbor query. The Measurement of search distance considered a circular location property of objects is required to accurately select the nodes which will be searched in the nearest neighbor query. In this paper, we propose the processing method of the nearest neighbor query be considered a circular location property of object where the search space consists of a circular domain and show its performance by experiments. The proposed method uses the circular minimum distance and the circular optimal distance which are the search measurements for optimizing the processing cost of the nearest neighbor query.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.23-31
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• 2015

A conventional missing value problem in the statistical analysis k-Nearest Neighbor(KNN) method are used for a simple imputation method. When one of the k-nearest neighbors is an extreme value or outlier, the KNN method can create a bias. In this paper, we propose a Weighted k-Nearest Neighbors(WKNN) imputation method that can supplement KNN's faults. A Monte-Carlo simulation study is also adapted to compare the WKNN method and KNN method using real data set.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.817-827
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• 2015

In this modern society, people are having a close relationship with smartphone. This makes easier for hackers to gain the user's information by installing the malware in the user's smartphone without the user's authority. This kind of action are threats to the user's privacy. The malware characteristics are different to the general applications. It requires the user's authority. In this paper, we proposed a new classification method of user requirements method by each application using the Principle Component Analysis(PCA) and Probabilistic K-Nearest Neighbor(PKNN) methods. The combination of those method outputs the improved result to classify between malware and general applications. By using the K-fold Cross Validation, the measurement precision of PKNN is improved compare to the previous K-Nearest Neighbor(KNN). The classification which difficult to solve by KNN also can be solve by PKNN with optimizing the discovering the parameter k and ${\beta}$. Also the sample that has being use in this experiment is based on the Contagio.

• The Pure and Applied Mathematics
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• v.24 no.4
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• pp.211-226
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• 2017

In this paper, machine learning models employed in various fields are discussed and applied to KOSPI200 stock index return forecasting. The results of hyperparameter analysis of the machine learning models are also reported and practical methods for each model are presented. As a result of the analysis, Support Vector Machine and Artificial Neural Network showed a better performance than k-Nearest Neighbor and Random Forest.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.53-62
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• 2006

Object recognition techniques using principal component analysis are disposed to be decreased recognition rate when lighting change of image happens. The purpose of this thesis is to propose an object recognition technique using new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. And the object recognition algorithm proposed here represents more enhanced recognition rate using improved k-Nearest Neighbor. In this thesis, we proposed an object recognition algorithm which creates object space by pre-processing and being learned image using histogram equalization and median filter. By spreading histogram of test image using histogram equalization, the effect to change of illumination is reduced. This method is stronger to change of illumination than basic PCA method and normalization, and almost removes effect of illumination, therefore almost maintains constant good recognition rate. And, it compares ingredient projected test image into object space with distance of representative value and recognizes after representative value of each object in model image is made. Each model images is used in recognition unit about some continual input image using improved k-Nearest Neighbor in this thesis because existing method have many errors about distance calculation.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.196-200
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• 2008

Recently, the query processing algorithm in spatial network database (SNDB) has attracted many interests. However, there is little research on route-based query processing algorithm in SNDB. Since the moving objects moves only in spatial networks, the route-based algorithm is very useful for LBS and Telematics applications. In this paper, we analyze In-Route Nearest Neighbor (IRNN) query, which is an typical one of route-based queries, and propose a new IRNN query processing algorithm with time constraint. In addition, we show from our performance analysis that our IRNN query processing algorithm with time constraint is better on retrieval performance than the existing IRNN query processing one.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.273-282
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• 2009

Missing data is one of the common problems in building analysis or prediction models using software project data. Missing imputation methods are known to be more effective missing data handling method than deleting methods in small software project data. While K nearest neighbor imputation is a proper missing imputation method in the software project data, it cannot use non-missing information of incomplete project instances. In this paper, we propose an approach to missing data imputation for numerical software project data by combining K nearest neighbor and maximum likelihood estimation; we also extend the average absolute error measure by normalization for accurate evaluation. Our approach overcomes the limitation of K nearest neighbor imputation and outperforms on our real data sets.