• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.915-926
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• 2020

Outlier detection methods help one to identify unusual instances in data that may correspond to erroneous, exceptional, or surprising events or behaviors. This work studies conditional outlier detection, a special instance of the outlier detection problem, in the context of incorrect data label identification. Unlike conventional (unconditional) outlier detection methods that seek abnormalities across all data attributes, conditional outlier detection assumes data are given in pairs of input (condition) and output (response or label). Accordingly, the goal of conditional outlier detection is to identify incorrect or unusual output assignments considering their input as condition. As a solution to conditional outlier detection, this paper proposes the ratio-based outlier scoring (ROS) approach and its variant. The propose solutions work by adopting conventional outlier scores and are able to apply them to identify conditional outliers in data. Experiments on synthetic and real-world image datasets are conducted to demonstrate the benefits and advantages of the proposed approaches.

• 한국멀티미디어학회논문지
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• 제23권6호
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• pp.747-755
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• 2020

In this paper, we deal with outlier data problems that occur when constructing a PM_2.5 fine dust forecasting system using a neural network. In general, when learning a neural network, some of the data are not helpful for learning, but rather disturbing. Those are called outlier data. When they are included in the training data, various problems such as overfitting occur. In building a PM_2.5 fine dust concentration forecasting system using neural network, we have found several outlier data in the training data. We, therefore, remove them, and then make learning 3 ways. Over_outlier model removes outlier data that target concentration is low, but the model forecast is high. Under_outlier model removes outliers data that target concentration is high, but the model forecast is low. All_outlier model removes both Over_outlier and Under_outlier data. We compare 3 models with a conventional outlier removal model and non-removal model. Our outlier removal model shows better performance than the others.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.333-344
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• 2012

We consider some statistical properties of the first order difference-based error variance estimator in nonparametric regression models with a single outlier. So far under an outlier(s) such difference-based estimators has been rarely discussed. We propose the first order difference-based estimator using the leave-one-out method to detect a single outlier and simulate the outlier detection in a nonparametric regression model with the single outlier. Moreover, the outlier detection works well. The results are promising even in nonparametric regression models with many outliers using some difference based estimators.

• ETRI Journal
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• 제41권4호
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• pp.437-451
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• 2019

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph-based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state-of-the-art methods in terms of accuracy and work well with various data types.

• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.163-168
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• 2009

Given the specific mean shift outlier model, the standard approaches to obtaining test statistics for outliers are discussed. Accuracy of outlier tests is investigated using subset curvatures. These subset curvatures appear to be reliable indicators of the adequacy of the linearization based test. Also, we consider obtaining graphical summaries of uncertainty in estimating parameters through confidence curves. The results are applied to the problem of assessing the accuracy of outlier tests.

• 한국경영과학회지
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• 제35권2호
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• pp.71-88
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• 2010

A density-based outlier detection such as an LOF (Local Outlier Factor) tries to find an outlying observation by using density of its surrounding space. In spite of several advantages of a density-based outlier detection method, the computational complexity of outlier detection has been one of major barriers in its application. In this paper, we present an LOF algorithm that can reduce computation time of a density based outlier detection algorithm. A kd-tree indexing and approximated k-nearest neighbor search algorithm (ANN) are adopted in the proposed method. A set of experiments was conducted to examine performance of the proposed algorithm. The results show that the proposed method can effectively detect local outliers in reduced computation time.

• 응용통계연구
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• 제27권7호
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• pp.1229-1241
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• 2014

이상점과 무응답이 동시에 존재하는 경우에는 무응답만 있는 경우에 비해 무응답 대체의 성능이 떨어지게 된다. 이러한 경우에는 먼저 이상점을 탐지하고, 탐지된 이상점의 영향력을 축소한 후 무응답 대체를 실시하여야 한다. 본 논문에서는 이상점의 영향력을 축소하여 무응답 대체법의 성능을 향상시키는 방법을 연구하였다. 이를 위해 She and Owen (2011)이 제안한 이상점 탐지법을 살펴보았고, 탐지된 이상점의 영향력을 줄이기 위한 방법으로 흔히 사용되는 가중치 조정법과 이상점 대체법을 살펴보았다. 또한 이상점 처리 방법을 적용한 무응답 대체법을 살펴보았으며 모의실험과 사례분석을 통하여 이상점 영향력 축소 효과를 살펴보았다.

• 방송공학회논문지
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• 제22권3호
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• pp.313-320
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• 2017

HEVC 표준에 적용된 인루프 필터링 방법인 De-blocking filter와 SAO(Sample Adaptive Offset)은 영상의 블록화 현상(Blocking artifacts)과 ringing artifacts를 효과적으로 제거하여 부호화 효율 및 주관적 화질 향상을 달성하였다. 하지만, 인루프 필터링 사용에도 불구하고, Corner outlier artifacts라 불리는 블록 경계의 모서리 부분에서 발생하는 2차원 형태의 블록화 현상은 쉽게 제거하지 못하는 문제점을 보인다. 본 논문에서는 Corner outlier 픽셀 탐색 및 판단과정, 해당 outlier 픽셀의 필터링 방법을 통하여 이러한 artifacts를 감소시키는 방법을 제안한다. 실험결과, 제안하는 방법은 화면간 예측에서 약간의 압축 효율 향상을 보였으며, 특히 영상의 주관적 화질 측면에서 개선된 효과를 나타내었다.

• 한국멀티미디어학회논문지
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• 제25권6호
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• pp.886-893
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• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2011년도 하계학술대회
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• pp.513-516
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• 2011

기존의 실시간 파노라마 합성 알고리즘에서는 매칭점과 입력 영상에서의 outlier를 구분하고 제거하기가 어렵기 때문에 노이즈가 많은 영상 또는 반복적인 패턴이 많은 영상에서 왜곡이 쉽게 발생하는 문제가 있다. 따라서 본 논문에서는 기존의 실시간 파노라마 합성 프레임웍에서 실시간 합성 조건을 만족시키면서 효과적으로 매칭점과 입력 영상에서의 outlier를 제거하는 방법을 제안한다. 이를 위해서 선형 모델에서 outlier을 제거하는 데 주로 사용되는 RANSAC 알고리즘을 실시간 파노라마 합성에서 사용되는 비선형 모델에 적용 가능하도록 수정하고 속도 향상을 위해서 사용되는 모델의 파라미터를 줄이는 방법을 제안한다. 이를 통하여 매칭점 중에 존재하는 outiler를 제거하고 전체 매칭점 중에서 inlier 비율을 이용하여 입력되는 영상시퀀스에서 outlier 영상을 제거하는 방법을 제안한다. 실험 결과 기존의 방법에 비해서 합성 결과의 왜곡이 줄어드는 것을 확인하였다.