Modelling the Outlier Detection Problem in ASP(Q)

2022 ◽  
pp. 15-23
Author(s):  
Pierpaolo Bellusci ◽  
Giuseppe Mazzotta ◽  
Francesco Ricca
Keyword(s):  
Outlier Detection ◽  
Detection Problem

Development for modification of Torgerson projection method using cumulative curve analysis in outlier detection problem for high-dimensional data

2020 ◽  
pp. 119-129
Author(s):  
Никита Сергеевич Олейник ◽  
Владислав Юрьевич Щеколдин
Keyword(s):  
Multidimensional Scaling ◽  
Outlier Detection ◽  
High Dimensional Data ◽  
Quality Data ◽  
Multidimensional Data ◽  
High Dimensional ◽  
Detection Problem ◽  
Gravity Center ◽  
Largest Eigenvalues ◽  
Cumulative Curves

Рассмотрена задача выявления аномальных наблюдений в данных больших размерностей на основе метода многомерного шкалирования с учетом возможности построения качественной визуализации данных. Предложен алгоритм модифицированного метода главных проекций Торгерсона, основанный на построении подпространства проектирования исходных данных путем изменения способа факторизации матрицы скалярных произведений при помощи метода анализа кумулятивных кривых. Построено и проанализировано эмпирическое распределение F -меры для разных вариантов проектирования исходных данных Purpose. Purpose of the article. The paper aims at the development of methods for multidimensional data presentation for solving classification problems based on the cumulative curves analysis. The paper considers the outlier detection problem for high-dimensional data based on the multidimensional scaling, in order to construct high-quality data visualization. An abnormal observation (or outlier), according to D. Hawkins, is an observation that is so different from others that it may be assumed as appeared in the sample in a fundamentally different way. Methods. One of the conceptual approaches that allow providing the classification of sample observations is multidimensional scaling, representing by the classical Orlochi method, the Torgerson main projections and others. The Torgerson method assumes that when converting data to construct the most convenient classification, the origin must be placed at the gravity center of the analyzed data, after which the matrix of scalar products of vectors with the origin at the gravity center is calculated, the two largest eigenvalues and corresponding eigenvectors are chosen and projection matrix is evaluated. Moreover, the method assumes the linear partitioning of regular and anomalous observations, which arises rarely. Therefore, it is logical to choose among the possible axes for designing those that allow obtaining more effective results for solving the problem of detecting outlier observations. A procedure of modified CC-ABOD (Cumulative Curves for Angle Based Outlier Detection) to estimate the visualization quality has been applied. It is based on the estimation of the variances of angles assumed by particular observation and remaining observations in multidimensional space. Further the cumulative curves analysis is implemented, which allows partitioning out groups of closely localized observations (in accordance with the chosen metric) and form classes of regular, intermediate, and anomalous observations. Results. A proposed modification of the Torgerson method is developed. The F1-measure distribution is constructed and analyzed for different design options in the source data. An analysis of the empirical distribution showed that in a number of cases the best axes are corresponding to the second, third, or even fourth largest eigenvalues. Findings. The multidimensional scaling methods for constructing visualizations of multi-dimensional data and solving problems of outlier detection have been considered. It was found out that the determination of design is an ambiguous problem.

Empirical estimation of the power of test in outlier detection problem

2019 ◽  
Vol 63 (1) ◽  
pp. 55-70
Author(s):  
Bahattin Erdogan ◽  
Serif Hekimoglu ◽  
Utkan Mustafa Durdag ◽  
Taylan Ocalan
Keyword(s):  
Outlier Detection ◽  
Detection Problem ◽  
Empirical Estimation ◽  
Power Of Test

An Efficient Distance and Density Based Outlier Detection Approach

2012 ◽  
Vol 155-156 ◽  
pp. 342-347 ◽  
Author(s):  
Xun Biao Zhong ◽  
Xiao Xia Huang
Keyword(s):  
Outlier Detection ◽  
Real World ◽  
High Performance ◽  
Detection Problem ◽  
Empirical Results ◽  
Detection Approach ◽  
Real World Datasets ◽  
Good Detection

In order to solve the density based outlier detection problem with low accuracy and high computation, a variance of distance and density (VDD) measure is proposed in this paper. And the k-means clustering and score based VDD (KSVDD) approach proposed can efficiently detect outliers with high performance. For illustration, two real-world datasets are utilized to show the feasibility of the approach. Empirical results show that KSVDD has a good detection precision.

scholarly journals Detection of Lesions Underlying Intractable Epilepsy on T1-Weighted MRI as an Outlier Detection Problem

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0161498 ◽  
Author(s):  
Meriem El Azami ◽  
Alexander Hammers ◽  
Julien Jung ◽  
Nicolas Costes ◽  
Romain Bouet ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Intractable Epilepsy ◽  
Detection Problem

Parkinsonism as an outlier detection problem

2013 ◽  
Vol 14 ◽  
pp. e161
Author(s):  
J. Kempfner ◽  
P. Jennum
Keyword(s):  
Outlier Detection ◽  
Detection Problem

Rapid Eye Movement Sleep Behavior Disorder as an Outlier Detection Problem

2014 ◽  
Vol 31 (1) ◽  
pp. 86-93 ◽  
Author(s):  
J. Kempfner ◽  
G. L. Sorensen ◽  
M. Nikolic ◽  
R. Frandsen ◽  
H. B. D. Sorensen ◽  
...  
Keyword(s):  
Eye Movement ◽  
Outlier Detection ◽  
Behavior Disorder ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Detection Problem ◽  
Sleep Behavior

scholarly journals Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

NeuroImage ◽  
2011 ◽  
Vol 58 (3) ◽  
pp. 793-804 ◽  
Author(s):  
Janaina Mourão-Miranda ◽  
David R. Hardoon ◽  
Tim Hahn ◽  
Andre F. Marquand ◽  
Steve C.R. Williams ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Outlier Detection ◽  
Support Vector ◽  
Patient Classification ◽  
Detection Problem ◽  
The One

scholarly journals MEOD: Memory-Efficient Outlier Detection on Streaming Data

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 458
Author(s):  
Ankita Karale ◽  
Milena Lazarova ◽  
Pavlina Koleva ◽  
Vladimir Poulkov
Keyword(s):  
Outlier Detection ◽  
Optimization Problem ◽  
Streaming Data ◽  
Local Correlation ◽  
Detection Problem ◽  
Swarm Optimization ◽  
Correlation Integral ◽  
Memory Time ◽  
Local Outlier ◽  
Memory Efficient

In this paper, a memory-efficient outlier detection (MEOD) approach for streaming data is proposed. The approach uses a local correlation integral (LOCI) algorithm for outlier detection, finding the outlier based on the density of neighboring points defined by a given radius. The radius value detection problem is converted into an optimization problem. The radius value is determined using a particle swarm optimization (PSO)-based approach. The results of the MEOD technique application are compared with existing approaches in terms of memory, time, and accuracy, such as the memory-efficient incremental local outlier factor (MiLOF) detection technique. The MEOD technique finds outlier points similar to MiLOF with nearly equal accuracy but requires less memory for processing.

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