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Comparing Methods for Measurement Error Detection in Serial 24-h Hormonal Data
2019
Journal of Biological Rhythms
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In this study, we aimed to compare performances of different methods for outlier detection in hormonal serial data. ...
Eyeballing detects outliers based on experts' knowledge, and the stepwise approach incorporates physiological knowledge with a statistical algorithm. ...
CONFLICT OF INTEREST STATEMENT The authors have no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. ...
doi:10.1177/0748730419850917
pmid:31187683
pmcid:PMC6637814
fatcat:eplza3ywxfdwbizj4sky7xwufq
Comparative Study of Clustering-Based Outliers Detection Methods in Circular-Circular Regression Model
2021
Sains Malaysiana
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This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. ...
The performances of the algorithms have been demonstrated using the simulation studies that consider several outlier scenarios with a certain degree of contamination. ...
aCKnOWLEDgEMEnTS The authors would like to thank the Ministry of Higher Education for providing financial support under Fundamental research grant no. ...
doi:10.17576/jsm-2021-5006-24
fatcat:frpblkrjtbdrtlwlop4l7tlbxe
Fair Outlier Detection Based on Adversarial Representation Learning
2022
Symmetry
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Our work focuses on studying the fairness of outlier detection. ...
We characterize the properties of fair outlier detection and propose an appropriate outlier detection method that combines adversarial representation learning and the LOF algorithm (AFLOF). ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/sym14020347
fatcat:6gxbm5c545av3dh3wtyel7pbme
Evaluation of Unsupervised Anomaly Detection Methods in Sentiment Mining
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Recent studies have been found to concentrate more on outlier detection for real time datasets. ...
Anomaly detection study is at present focuses on the expansion of innovative machine learning methods and on enhancing the computation time. ...
In this study, a comparative evaluation of various anomaly detection algorithms such as density based, statistical based and cluster based anomaly detection methods is presented. ...
doi:10.35940/ijitee.i8012.078919
fatcat:spcluqy6obcvth6qtm63rn3i2q
Survey on Outlier Detection in Data Mining
2013
International Journal of Computer Applications
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Data Mining is used to extract useful information from a collection of databases or data warehouses. In recent years, Data Mining has become an important field. ...
This paper also presents various techniques used by different researchers to detect outliers and present the efficient result to the user. ...
There are various methods used now days to detect outlier so here we will have a Table 1 which shows the comparative study of different algorithms used by different researchers. ...
doi:10.5120/11506-7223
fatcat:bopcgn7atjdxzixytw2l53zivm
A Novel Outlier Detection Applied to an Adaptive K-Means
2019
International Journal of Machine Learning and Computing
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To address the problems, this study proposed a new method of initial centers selection based on data density and a novel approach of outlier detection based on data distance. ...
For the outlier detection system, I measured the system performance by using a confusion matrix. ...
A study by [20] introduced a Local outlier factor (LOF) algorithm approach on Graphics processing units for Intrusion detection systems. ...
doi:10.18178/ijmlc.2019.9.5.841
fatcat:d4dg4suwrfalfidfmyhd74ia5e
Improving Outliers Detection in Data Streams using LiCS and Voting
2019
Journal of King Saud University: Computer and Information Sciences
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Experiments on real data proves that it outperforms discussed algorithms in terms of accuracy, precision and sensitivity in detecting outliers. ...
In this paper, first, we improve the capacity to detect outliers of both microclusters based algorithms (MCOD) and distance-based algorithms (Abstract-C and Exact-Storm) known for their performance. ...
Outlier Detection: the outliers are detected by executing in parallel the selected algorithms (MCOD, Exact-Storm and
Table 2 2 Comparing the improved version of algorithms with their original versions ...
doi:10.1016/j.jksuci.2019.08.003
fatcat:gdc5zizmsfedtacprlwu7hji2m
The multiple outliers detection using agglomerative hierarchical methods in circular regression model
2017
Journal of Physics, Conference Series
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In this study, we compared the performance of single-linkage method with another agglomerative hierarchical method, namely average linkage for detecting outlier in circular regression model. ...
Two agglomerative hierarchical clustering algorithms for identifying multiple outliers in circular regression model have been developed in this study. ...
Acknowledgement The Ministry of Higher Education Malaysia and Universiti Malaysia Pahang are acknowledged for the financial support received for this study (FRGS, RDU 160117). ...
doi:10.1088/1742-6596/890/1/012152
fatcat:ohrsl75bnngg5jgjhrtugp6qoi
COMPARATIVE STUDY OF KSVDD AND FSVM FOR CLASSIFICATION OF MISLABELED DATA
2016
International Journal of Research in Engineering and Technology
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Outlier detection is the important concept in data mining. These outliers are the data that differ from the normal data. Noise in the application may cause the misclassification of data. ...
SVDD algorithm is used for classification of data with likelihood values. ...
The algorithm is most widely used for outlier detection because of its performance with respect to other learning algorithm. The detection of these outliers plays a major role in many areas. ...
doi:10.15623/ijret.2016.0503018
fatcat:4howt2pnj5gx3epzxm3q6awt3m
Rough K-means Outlier Factor Based on Entropy Computation
2014
Research Journal of Applied Sciences Engineering and Technology
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This study proposes the new outlier detection method based on the hybrid of the Rough K-Means clustering algorithm and the entropy computation. ...
Many studies of outlier detection have been developed based on the cluster-based outlier detection approach, since it does not need any prior knowledge of the dataset. ...
ACKNOWLEDGMENT I would like to acknowledge Atma Jaya University in Yogyakarta, Indonesia and UKM Grant No.UKM-DLP-2011-020 for the financial support of this study project. ...
doi:10.19026/rjaset.8.986
fatcat:lxw5jhpbjfcgpnoem7ih4nthwq
A Two-Level Approach based on Integration of Bagging and Voting for Outlier Detection
2020
Journal of Data and Information Science
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AbstractPurposeThe main aim of this study is to build a robust novel approach that is able to detect outliers in the datasets accurately. ...
The proposed approach, named Bagged and Voted Local Outlier Detection (BV-LOF), benefits from the Local Outlier Factor (LOF) as the base algorithm and improves its detection rate by using ensemble methods.FindingsSeveral ...
(ii) This is the first study that runs the LOF algorithm with
Research Paper Journal of Data and Information Science A Two-Level Approach based on Integration of Bagging and Voting for Outlier Detection ...
doi:10.2478/jdis-2020-0014
fatcat:qgvpepz6lrdk7fy3wa7oy72wma
MSD-Kmeans: A Novel Algorithm for Efficient Detection of Global and Local Outliers
[article]
2019
arXiv
pre-print
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We compare the performance indicators of MSD-Kmeans with those of other outlier detection algorithms, such as MSD, K-means, Z-score, MIQR and LOF, and prove that the proposed MSD-Kmeans algorithm achieves ...
Outlier detection is a technique in data mining that aims to detect unusual or unexpected records in the dataset. ...
A few studies proposed improvements of K-means for outlier detection. ...
arXiv:1910.06588v1
fatcat:ybpry75gn5d3rc25yqmvfsq7wi
Comparative Study of Outlier Detection Algorithms
2016
International Journal of Computer Applications
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This paper covers a study of various outlier detection algorithms like Statistical based outlier detection, Depth based outlier detection, Clustering based technique, Density based outlier detection etc ...
Comparison study of these outlier detection methods is done to find out which of the outlier detection algorithms are more applicable on high dimensional data. ...
It is concluded that performance of clustering algorithms is comparatively better than other outlier detection algorithms on huge data sets. ...
doi:10.5120/ijca2016911176
fatcat:os25y5n3t5f7lmdvvjb6mzk64e
An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions
2021
Information
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In this light, the authors of this paper present, in detail, an innovative Abnormal Behavior Detection Engine, which also encompasses a knowledge base visualization functionality focusing on financial ...
The framework presented in this paper combines algorithms and tools that are used to correlate different pieces of data leading to the discovery and recording of forensic evidence. ...
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the policy of the PREVISION project. ...
doi:10.3390/info12010034
fatcat:k4t2hn3ienee3pqhgjnhnefudy
Outlier Detection Methods and the Challenges for their Implementation with Streaming Data
2020
Journal of Mobile Multimedia
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This is why the study suggests that there is a need of a hybrid approach that combines classical algorithms and artificial intelligence algorithm to provide efficient solution for outlier detection of ...
Outlier detection has been a generally examined issue and highly used in a varied range of spaces. ...
So the study suggests that there is a need of a hybrid approach that combines classical algorithms and artificial intelligence algorithms in order to provide efficient solution for outlier detection of ...
doi:10.13052/jmm1550-4646.1635
fatcat:fnrb55vouba43lszw7mmsrfgp4
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