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Adaptive Outlier Detection and Classification for Online Soft Sensor Update

Hector J. Galicia, Q. Peter He, Jin Wang
2012 IFAC Proceedings Volumes  
Because the PLS algorithms are sensitive to outliers in the dataset, outlier detection and handling plays a critical role in the development of the PLS based soft sensors.  ...  In this work, we develop a multivariate approach for online outlier detection.  ...  reconstructed SPE indices of new measurements.  ... 
doi:10.3182/20120710-4-sg-2026.00091 fatcat:vlkj6md2ojhuzj2yc3mt5bgecm

A comparison on two discordancy tests to detect outlier in von mises (VM) sample

Fatin Najihah Badarisam, Adzhar Rambli, Mohammad Illyas Sidik
2020 Indonesian Journal of Electrical Engineering and Computer Science  
As a result, the RCDu Statistic perform well in detecting a correct single outlier.  ...  The performance tests of RCDu Statistic and </span><span>𝐺</span><sub><span>1</span></sub><span> Statistic</span><span> are tested in outlier proportion of correct detection, masking and swamping effect  ...  the fund of this study.  ... 
doi:10.11591/ijeecs.v19.i1.pp156-163 fatcat:ayjbc6efrfgfdoghxf5l5dk4ka

A Novel Distance for Clustering to Support Mixed Data Attributes and Promote Data Reliability and Network Lifetime in Large Scale Wireless Sensor Networks

N. ChitraDevi, V. Palanisamy, K. Baskaran, S. Prabeela
2012 Procedia Engineering  
In this paper we have evaluated the performance of proposed algorithm in terms of false alarm rate, false positive rate and detection rate.  ...  To address the issues of energy minimization and data reliability, we propose a distributed agglomerative cluster based anomaly detection algorithm termed DACAD to detect the faulty readings based on kNN  ...  A false alarm rate is defined as fraction of number of normal measurements detected as outliers by total number of normal outliers detected.  ... 
doi:10.1016/j.proeng.2012.01.913 fatcat:s6tx55qqbbenhf57xd657ifrti

A Fault-Tolerant Data Fusion Method of MEMS Redundant Gyro System Based on Weighted Distributed Kalman Filtering

Binhan Du, Zhiyong Shi, Jinlong Song, Huaiguang Wang, Lanyi Han
2019 Micromachines  
The application of the Micro Electro-mechanical System (MEMS) inertial measurement unit has become a new research hotspot in the field of inertial navigation.  ...  Simulation experiments are conducted to test the performance of the new method with different types of anomalies.  ...  Figure 5 . 5 The influence of outlier eliminating. (a) The measurement signals; (b) the detection function. Figure 5 . 5 The influence of outlier eliminating.  ... 
doi:10.3390/mi10050278 pmid:31035461 pmcid:PMC6562494 fatcat:fcufsdjecvaajbvd4sskdickda

Outlier Detection Scoring Measurements Based on Frequent Pattern Technique

Aiman Moyaid Said, Dhanapal Durai Dominic, Brahim Belhaouari Samir
2013 Research Journal of Applied Sciences Engineering and Technology  
The comparisons of the outlier detection scoring measurements are based on the detection effectiveness.  ...  Recently, a new trend for detecting the outlier by discovering frequent patterns (or frequent item sets) from the data set has been studied.  ...  Another problem addressed by this research was the effect of the dimensionality on the accuracy of the outlier detection. A new outlier measurement was proposed.  ... 
doi:10.19026/rjaset.6.3954 fatcat:y2rkzcsjmzckhctbuvlf6u4xum

An Isolation-based Distributed Outlier Detection Framework using Nearest Neighbor Ensembles for Wireless Sensor Networks

Zhong-Min Wang, Guo-Hao Song, Cong Gao
2019 IEEE Access  
The existing outlier detection methods have some drawbacks, such as extra resource consumption introduced by the size growth of a local detector, poor performance of combination methods of local detectors  ...  A new combination method taking advantage of the spatial correlation among sensor nodes for local detectors is presented. The method is based on the weighted voting idea.  ...  In specific, when a new measurement x is obtained by sensor node sn, the local detector of sn calculates its outlier score.  ... 
doi:10.1109/access.2019.2929581 fatcat:vuys2bvcxjadzea3voyr2wg67u

A Novel Outlier Detection Applied to an Adaptive K-Means

Sarunya Kanjanawattana, Compiter Engineering, Institute of Engineering, Suranaree University of Technology, Nakhonratchasima 30000, Thailand
2019 International Journal of Machine Learning and Computing  
For the outlier detection system, I measured the system performance by using a confusion matrix.  ...  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.  ...  Furthermore, I introduce a new method of outlier detection by using a basic idea of the distance-based method.  ... 
doi:10.18178/ijmlc.2019.9.5.841 fatcat:d4dg4suwrfalfidfmyhd74ia5e

Diagnosis for Early Stage of Breast Cancer using Outlier Detection Algorithm Combined with Classification Technique

2019 International Journal of Engineering and Advanced Technology  
Size of the dataset has to shrink gently the computation time also reduced. The second stage, the outlier detection (OD) algorithm has used to detect the outliers from the cancer dataset.  ...  The proposed method has a process of three stages. First, data objects are grouped into clusters using k-means clustering algorithm.  ...  Performance Evaluation In the stability of the proposed method, the performance is measured and evaluated on datasets.  ... 
doi:10.35940/ijeat.b4514.129219 fatcat:qrumy7lsorhllcnn65lkxemvja

On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights [article]

Lingxiao Zhao, Leman Akoglu
2021 arXiv   pre-print
Interestingly, ROC-AUCs on these two variants approximately sum to 1 and their performance gap is amplified with increasing propagations for a certain family of propagation based outlier detection models  ...  It is common practice of the outlier mining community to repurpose classification datasets toward evaluating various detection models.  ...  Thus, having a clear understanding of this issue becomes critical for developing new models for GLOD.  ... 
arXiv:2012.12931v3 fatcat:dcfym7jborcczkpldl5vqzeamq

Rough K-means Outlier Factor Based on Entropy Computation

Djoko Budiyanto Setyohadi, Azuraliza Abu Bakar, Zulaiha Ali Othman
2014 Research Journal of Applied Sciences Engineering and Technology  
This study proposes the new outlier detection method based on the hybrid of the Rough K-Means clustering algorithm and the entropy computation.  ...  We introduce the outlier degree measure namely the entropy outlier factor for the cluster based outlier detection.  ...  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

An online outlier detection technique for wireless sensor networks using unsupervised quarter-sphere support vector machine

Zhang Yang, Nirvana Meratnia, Paul Havinga
2008 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing  
network to a minimum.  ...  kernel functions compared to an earlier offline outlier detection technique designed for wireless sensor networks.  ...  Our future research includes online updating the boundary of normal data with arrival of new data measurements, and evaluating outlier detection performance while distinguishing between events and errors  ... 
doi:10.1109/issnip.2008.4761978 fatcat:zrj3cs2o7jb2harq7y6c7p634y

Comparative Study of Clustering-Based Outliers Detection Methods in Circular-Circular Regression Model

Siti Zanariah Satari, Nur Faraidah Muhammad Di, Yong Zulina Zubairi, Abdul Ghapor Hussin
2021 Sains Malaysiana  
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

Improving Outliers Detection in Data Streams using LiCS and Voting

Fatima-Zahra Benjelloun, Ahmed Oussous, Amine Bennani, Samir Belfkih, Ayoub Ait Lahcen
2019 Journal of King Saud University: Computer and Information Sciences  
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.  ...  Experiments on real data proves that it outperforms discussed algorithms in terms of accuracy, precision and sensitivity in detecting outliers.  ...  It compares the hybrid solution with each of the original version of MCOD, Abstract-C and Exact-Strom by measuring the known performance metrics commonly used for outlier detection namely (accuracy, recall  ... 
doi:10.1016/j.jksuci.2019.08.003 fatcat:gdc5zizmsfedtacprlwu7hji2m

Ranking with Distance based Outlier Detection Techniques: A Survey

Jitendra R.Chandvanya, Rajanikanth Aluvalu
2014 International Journal of Computer Applications  
Here we will study some outlier detection technique which are mainly based on distance-based outlier detection with ranking approach and give some idea about the new technique which we will implement in  ...  Outlier Detection is very much popular in Data Mining field and it is an active research area due to its various applications like fraud detection, network sensor, email spam, stock market analysis, and  ...  Mehrotra define Rank-Based Outlier Detection, here in this paper author propose new approach for outlier detection, based on this new ranking measure the purpose of this measure is whether a point is important  ... 
doi:10.5120/15505-4207 fatcat:kacyax7u7zhnnjluqmvxfgs2l4

Detecting Projected Outliers in High-Dimensional Data Streams [chapter]

Ji Zhang, Qigang Gao, Hai Wang, Qing Liu, Kai Xu
2009 Lecture Notes in Computer Science  
In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded  ...  Sparse Subspace Template (SST), a set of subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively.  ...  outlier-ness of data points based on a single criterion, SPOT adopts a more flexible framework allowing for the use of multiple measures for outlier detection.  ... 
doi:10.1007/978-3-642-03573-9_53 fatcat:jqg73cdn35e7rj7wiwqtealbrm
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