Results 51 to 60 of about 4,573,641 (310)

An integrated approach for identifying wrongly labelled samples when performing classification in microarray data. [PDF]

PLoS ONE, 2012
Using hybrid approach for gene selection and classification is common as results obtained are generally better than performing the two tasks independently.
Yuk Yee Leung, Chun Qi Chang, Yeung Sam Hung   +2 more
doaj   +1 more source

Clustering-Based Outlier Detection Technique Using PSO-KNN

Journal of Applied Science and Engineering, 2023
In this work, we present an unsupervised machine learning algorithm for outlier detection by integrating Particle Swarm Optimization (PSO) and the K-nearest neighbor (KNN) technique.
Sushilata D. Mayanglambam, Rajendra Pamula, Shi-Jinn Horng   +2 more
doaj   +1 more source

Behavior-Based Outlier Detection for Network Access Control Systems [PDF]

, 2019
Network Access Control (NAC) systems manage the access of new devices into enterprise networks to prevent unauthorised devices from attacking network services. The main difficulty with this approach is that NAC cannot detect abnormal behaviour of devices
Dhillon Gurjeet Singh, Eskin Eleazar, Kang Dongwan, Kim T.   +3 more
core   +1 more source

RSSI-Based Localization Schemes for Wireless Sensor Networks Using Outlier Detection

J. Sens. Actuator Networks, 2021
The received signal strength indicator (RSSI) of RF signals is a cost-effective solution for distance estimation, which makes it a practical choice for localization schemes in wireless sensor networks (WSN).
N. Chuku, Asis Nasipuri
semanticscholar   +1 more source

Comprehensive Outlier Detection in Wireless Sensor Network with Fast Optimization Algorithm of Classification Model

International Journal of Distributed Sensor Networks, 2015
Since the nonstationary distribution of the detected objects is general in the real world, the accurate and efficient outlier detection for data analysis within wireless sensor network (WSN) is a challenge.
Haiqing Yao, Heng Cao, Jin Li
doaj   +1 more source

Kernel outlier detection

Journal of Data Science, Statistics, and Visualisation
new anomaly detection method called kernel outlier detection (KOD) is proposed.It is designed to address challenges of outlier detection in high-dimensionalsettings. The aim is to overcome limitations of existing methods, such as dependenceon distributional assumptions or on hyperparameters that are hard to tune.KOD starts with a kernel transformation,
Can Hakan Dagidir, Mia Hubert, Peter J. Rousseeuw   +2 more
openaire   +2 more sources

New approaches and technical considerations in detecting outlier measurements and trajectories in longitudinal children growth data

BMC Medical Research Methodology, 2023
Background Growth studies rely on longitudinal measurements, typically represented as trajectories. However, anthropometry is prone to errors that can generate outliers.
Paraskevi Massara, Arooj Asrar, Celine Bourdon, Moses Ngari, Charles D. G. Keown-Stoneman, Jonathon L. Maguire, Catherine S. Birken, James A. Berkley, Robert H. J. Bandsma, Elena M. Comelli   +9 more
doaj   +1 more source

Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases. [PDF]

, 2017
PURPOSE: The purpose of this study was to apply statistical metrics to identify outliers and to investigate the impact of outliers on knowledge-based planning in radiation therapy of pelvic cases.
Aggarwal, Appenzoller, Barnett, Boutilier, Chanyavanich, Delaney, Fogliata, Hawkins, Hodge, Lian, Motulsky, Osborne, Pardoe, Sheng, Sheng, Tol, Wang, Whittingham, Wu, Yang, Youden, Yuan, Yuan, Zhu   +23 more
core   +2 more sources

Advancements of Outlier Detection: A Survey

EAI Endorsed Transactions on Scalable Information Systems, 2013
Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large datasets. In this paper, we present a survey of outlier detection techniques to reflect the recent advancements
Ji Zhang
doaj   +1 more source

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

IEEE Transactions on Automation Science and Engineering
In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the ...
Jingzhong Fang, Zidong Wang, Weibo Liu, S. Lauria, Nianyin Zeng, C. Prieto, F. Sikström, Xiaohui Liu   +7 more
semanticscholar   +1 more source