Results 11 to 20 of about 13,715 (249)
Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
Applied Sciences, 2021 Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns.
Michael Heigl +3 more
doaj +1 more source
A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams
Big Data and Cognitive Computing, 2020 Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning.
Omar Alghushairy +3 more
doaj +1 more source
Applied Sciences, 2023 A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc.
Yuehua Huang +4 more
doaj +1 more source
Outlier detection in BLAST hits [PDF]
Algorithms for Molecular Biology, 2018 An important task in a metagenomic analysis is the assignment of taxonomic labels to sequences in a sample. Most widely used methods for taxonomy assignment compare a sequence in the sample to a database of known sequences. Many approaches use the best BLAST hit(s) to assign the taxonomic label.
Nidhi Shah +2 more
openaire +6 more sources
Attribute Grouping-based Categorical Outlier Detection Using Isolation Forest Ensemble Strategy [PDF]
Jisuanji kexueAttribute grouping is one of the effective steps in high-dimensional outlier detection,but the current ensemble strategies in attribute grouping-based outlier detection only take into account the local outlier information within each attribute group,and ...
SONG Yijing, ZHANG Jifu
doaj +1 more source
Detecting Outliers in Non-IID Data: A Systematic Literature Review
IEEE Access, 2023 Outlier detection (outlier and anomaly are used interchangeably in this review) in non-independent and identically distributed (non-IID) data refers to identifying unusual or unexpected observations in datasets that do not follow an independent and ...
Shafaq Siddiqi +3 more
doaj +1 more source
Review of Outlier Detection Algorithms [PDF]
Jisuanji kexueOutlier detection,as an important research direction in the field of data mining,aims to discover data points in a dataset that are different from the majority and have potential analytical value,assistresearchers in identifying potential issues in the ...
KONG Lingchao, LIU Guozhu
doaj +1 more source
An Incremental Local Outlier Detection Method in the Data Stream
Applied Sciences, 2018 Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging.
Haiqing Yao +3 more
doaj +1 more source
A Novel Outlier Detection Model for Vibration Signals Using Transformer Networks
IEEE Access, 2022 Outlier detection in vibration signals can play an important role in addressing the issue of structural or environmental changes during vibration testing. In this study, a transformer-based model for outlier detection is proposed.
Ruiheng Zhang +4 more
doaj +1 more source
A Distributed Solution for Privacy Preserving Outlier Detection [PDF]
, 2011 In this paper, we study some parties - each has a private data set - want to conduct the outlier detection on their joint data set, but none of them want to disclose its private data to the other parties.
Luong, The Dung, Ho, Tu Bao
core +1 more source