EBOD: An ensemble-based outlier detection algorithm for noisy datasets

B Ouyang, Y Song, Y Li, G Sant, M Bauchy - Knowledge-Based Systems, 2021 - Elsevier
Real-world datasets often comprise outliers (eg, due to operational error, intrinsic variability
of the measurements, recording mistakes, etc.) and, hence, require cleansing as a …

Outlier detection in sensor data using ensemble learning

N Iftikhar, T Baattrup-Andersen, FE Nordbjerg… - Procedia Computer …, 2020 - Elsevier
Analyzing sensor data from a production environment is quite challenging because of the
high-dimensional nature of the data. In addition, the generated data is in the form of time …

An efficient ensemble framework for outlier detection using bio-inspired algorithm

PS Femi, SG Vaidyanathan - International Journal of Bio …, 2022 - inderscienceonline.com
Outliers are unexpected observations present in data that has to be identified systematically
to prevent from catastrophic effects. The detection of outliers plays a crucial role in many …

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges

A El Saer, L Grammatikopoulos, G Sfikas, G Karras… - Sensors, 2024 - mdpi.com
Vehicle exterior inspection is a critical operation for identifying defects and ensuring the
overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as …

Hybrid outlier detection in healthcare datasets using DNN and one class-SVM

R Thomas, JE Judith - 2020 4th International Conference on …, 2020 - ieeexplore.ieee.org
In general, a dataset may contain a minor portion of data objects, whose properties are not
identical to the majority of its data objects. Such data objects are called outliers. Most of the …

[PDF][PDF] Hyperplane Clustering of the Data in the Vector Space of Features Based on Pseudo Inversion Tools.

I Krak, H Kudin, V Kasianiuk, M Efremov - ProfIT AI, 2021 - ceur-ws.org
Method of the hyperplane data clustering in the vector space characteristics features based
on the results of the theory of perturbation of pseudo-inverse and projective matrices and …

Method of features analysis on transition data

E Manziuk, O Barmak, I Krak… - 2021 IEEE 3rd …, 2021 - ieeexplore.ieee.org
Detection of atypical data and outliers is an important and difficult task. Data that are
considered atypical in turn are characterized by a set of features that determine their …

Selective combination based on diversity-accuracy balance in outlier ensembles

L Shi, C Zhu - 2020 IEEE 22nd International Conference on …, 2020 - ieeexplore.ieee.org
In the outlier detection task, unsupervised detection methods have been widely applied due
to the absence of ground truth. Ensemble method is an emerging topic that has been studied …

A Method for Fast Outlier Detection in High Dimensional Database Log

X Song, Y Wang, L Zhu, W Ji, Y Du… - … on Networking and …, 2021 - ieeexplore.ieee.org
An easy to implement and effective outlier detection method is proposed in this paper, which
is a two-stage process combining the kd-tree structure and the Isolation Forest (Forest) …

Теоретичні та прикладні засади інтелектуальної інформаційної технології отримання довірчих рішень за людиноцентрованим підходом

ЕА Манзюк - 2022 - elar.khmnu.edu.ua
Анотація У дисертаційній роботі вирішено актуальну науково-прикладну проблему
недостатнього рівня довіри до інтелектуальних інформаційних технологій в частині …