RKDOS: A Relative Kernel Density-based Outlier Score

Wahid, Abdul; Rao, A. C.

doi:10.1080/02564602.2019.1647804

Cited by 8 publications

(9 citation statements)

References 21 publications

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“…Wahid and Rao have recently proposed three different densitybased methods, shortly named RKDOS [14], ODRA [15] and NaNOD [16]. RKDOS is mostly similar to RDOS, in which by using both kNN and RNN of each point, the density around it is estimated employing a Weighted Kernel Density Estimation (WKDE) strategy with a flexible kernel width.…”

Section: Density-based Methodsmentioning

confidence: 99%

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SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets

Nozad

Haeri

Folino

2021

Knowledge-Based Systems

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Section: Density-based Methodsmentioning

confidence: 99%

“…Here, we name such score obtained out of our proposed approach, SDCOR, which stands for ''Scalable Density-based Clustering Outlierness Ratio''. 14…”

Section: Scoringmentioning

confidence: 99%

SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets

Nozad

Haeri

Folino

2021

Knowledge-Based Systems

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“…It then analysed the fluctuation of that point compared to other points in an R.O.I. by using the average density fluctuation [50,51] to evaluate the outliner indication from that point. This method leads us to propose a new framework that estimates without the need for an influence dataset to depict variety distribution to overcome the problem of predetermining the extent of variability.…”

Section: Related Workmentioning

confidence: 99%

Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach

Hasan

Yusuf

Rahim

et al. 2023

Plants

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The current methods of classifying plan disease images are mainly affected by the training phase and the characteristics of the target dataset. Collecting plant samples during different leaf life cycle infection stages is time-consuming. However, these samples may have multiple symptoms that share the same features but with different densities. The manual labelling of such samples demands exhaustive labour work that may contain errors and corrupt the training phase. Furthermore, the labelling and the annotation consider the dominant disease and neglect the minor disease, leading to misclassification. This paper proposes a fully automated leaf disease diagnosis framework that extracts the region of interest based on a modified colour process, according to which syndrome is self-clustered using an extended Gaussian kernel density estimation and the probability of the nearest shared neighbourhood. Each group of symptoms is presented to the classifier independently. The objective is to cluster symptoms using a nonparametric method, decrease the classification error, and reduce the need for a large-scale dataset to train the classifier. To evaluate the efficiency of the proposed framework, coffee leaf datasets were selected to assess the framework performance due to a wide variety of feature demonstrations at different levels of infections. Several kernels with their appropriate bandwidth selector were compared. The best probabilities were achieved by the proposed extended Gaussian kernel, which connects the neighbouring lesions in one symptom cluster, where there is no need for any influencing set that guides toward the correct cluster. Clusters are presented with an equal priority to a ResNet50 classifier, so misclassification is reduced with an accuracy of up to 98%.

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“…We select health-related dataset with the size of 5000, 10000, 20000, and 50000 randomly, and experiments are performed in selected dataset in order to demonstrate that the proposed outlier detection algorithm is more efficient in running time. We choose relative kernel density-based outlier score (RKDOS) (Wahid & Rao, 2020), influenced outlierness (INFLO) (Zhou et al, 2019), and local distance-based outlier detection factor (LDOF) (Radovanović et al, 2015) for comparison.…”

Section: Data Sourcementioning

confidence: 99%

AI for Health-Related Data Modeling

Cheng¹

2022

International Journal of Information System Modeling and Design

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Data modeling of health-related data from Data Center (DC) has positive effects for health monitoring, disease prevention, and healthcare research. However, health-related data has the characteristics of huge, high-dimensional, and non-normalized, which are not beneficial to direct analysis, so data needs to be preprocessed before data modeling. This paper focuses on the features of health-related data, and outlier detection during data preprocessing is studied. Meanwhile, we propose an improved algorithm for health-related data based outlier detection. The experimental results reveal that the proposed outlier detection algorithm has a smaller running time, and more outliers are detected compared to three baselines. In addition, local importance based random forest feature selection algorithm is proposed to measure the importance of each feature. The experimental results indicate that the proposed algorithm can select optimal feature subset to apply health-related data.

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RKDOS: A Relative Kernel Density-based Outlier Score

Cited by 8 publications

References 21 publications

SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets

SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets

Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach

AI for Health-Related Data Modeling

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