• 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.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1932-1935
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• 2003

본 논문에서는 MPEG-2, MPEG-4, H.263 등에서 블록정합을 위해 사용되는 움직임 추정(Motion Estimation) 기법에서 적응적 탐색 범위를 기존의 알고리듬에 적용시킴으로써 계산량을 줄이고 화질도 개선하는 방법을 제안한다 제안된 알고리듬은 먼저 이웃한 움직임 벡터(Motion Vector)의 위치를 이용하여 예상된 움직임 벡터를 찾고 이 예상된 움직임 벡터의 X, Y 값의 크기를 작은 값, 중간 값, 큰 값, 세 가지로 분류해서 탐색범위를 적응적으로 변화시켜 움직임 벡터가 있을 확률이 큰 범위를 집중적으로 찾는다 그리고 각 분류에서 작은 값일 때는 전역 탐색을 적용하고 큰 값일 때는 기존의 알고리듬을 적용시키고 중간 값 일 때는 3단계탐색 기법을 적용시켜 더 적합한 움직임 벡터를 찾도록 하였다. 그리고 작은 값 일 때 구해진 움직임 벡터의 SAD(Sum of Absolute Difference) 값과 이웃한 움직임 벡터의 SAD값을 비교해 국소점에 빠졌다고 판단이 되면 다시 탐색 범위를 조정해서 움직임 벡터를 구함으로써 국소점에 빠지는 경우를 줄였다.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.271-273
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• 2021

본 논문은 이동노드의 클러스터링내에서 보다 효율적인클러스터링을 제공하고 유지하기위한 딥러닝의 선형회귀적 적응적 보정가중치에 따른 군집적 알고리즘을 제안한다. 대부분의 클러스터링 군집데이터를 처리함에 있어 상호관계에 따른 분류체계가 제공된다. 이러한 경우 이웃한 이동노드중 목적노드와는 연결가능성이 가장높은 이동노드를 클러스터내에서 중계노드로 선택해야 한다. 본 연구에서는 이러한 상황정보를 이해하고 동적이동노드간 속도와 방향속성정보간의 상관관계의 친밀도를 고려한 자율학습기반의 회귀적 모델에서 적응적 가중치에 따른 분류를 제시한다. 본 논문에서는 이러한 상황정보를 이해하고 클러스터링을 유지할 수 있는 자율학습기반의 적응적 가중치에 따른 딥러닝 모델을 제시 한다.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.193-206
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• 2008

Medical diagnosis can be considered a classification task which classifies disease types from patient's condition data represented by a set of pre-defined attributes. This study proposes a hybrid genetic algorithm based classification method to develop classifiers for multidimensional pattern classification problems related with medical decision making. The classification problem can be solved by identifying separation boundaries which distinguish the various classes in the data pattern. The proposed method fits a finite number of regional agents to the data pattern by combining genetic algorithms and local adaptive operations. The local adaptive operations of an agent include expansion, avoidance and relocation, one of which is performed according to the agent's fitness value. The classifier system has been tested with well-known medical data sets from the UCI machine learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.1
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• pp.39-45
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• 2007

In this paper, we present a new postprocessing algorithm using interpolation and signal adaptive filter according to the each block characteristic which is acquired in block classification process. We applied blocking artifact reduction algorithm for four neighbor low frequency block and ringing artifacts is removed with preserving edges by applying a signal adaptive filter in high frequency block based on edge map. The computer simulation results confirmed a better performance by the proposed method in both the subjective and objective image qualities.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.201-206
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• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1093-1102
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• 2010

Even though the k-Nearest Neighbors Classification(KNNC) is one of the popular non-parametric classification methods, it does not consider the local features and class information for each observation. In order to overcome such limitations, several methods have been developed such as Adaptive Nearest Neighbors Classification(ANNC) and Modified k-Nearest Neighbors Classification(MKNNC). In this paper, we propose the Modified Adaptive Nearest Neighbors Classification(MANNC) that employs the advantages of both the ANNC and MKNNC. Through a real data analysis and a simulation study, we show that the proposed MANNC outperforms other methods in terms of classification accuracy.

• Journal of the Korean Society of Child Welfare
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• no.27
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• pp.69-93
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• 2008

The study tried to identify suicidal factors of adolescences by analyzing effect factors of adolescences' suicide. Demographic factor, Self-respect, depression, suicidal ideation, and school adjustment scale, Social support, self-regulation, and problem solving scale were analyzed as effect factors. 307 junior high and high school students in Pusan city were surveyed by community education experts for this research. As a result, there are significant differences between high suicidal risk group and low suicidal group in all subcategories including economic status. As for the correlation analysis between risk factors and preventive factors, variables, it was analyzed that each variable was correlated to other variables. In addition, multiple regression analysis was conducted to find out affecting factors of adolescence suicide categorized into demographic factor, personal factor, school factor, and social factor. As a result, it was founded that the affecting factors of adolescence suicide were school adjustment, problem solving ability, suicidal ideation, depression and neighborsupport, and self-regulation. That is, to prevent adolescence suicide, the adolescence who are exposed to suicidal risk should be identified by measuring the degree of suicidal ideation, strengthened their school adjustment, and then conducted group therapy to strengthen their problem solving ability and self-regulation. In addition, adolescence suicide can be prevented by strengtheningsocial support for adolescences.