Results 41 to 50 of about 258,865 (274)

Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps

Big Data and Cognitive Computing, 2020
Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection.
Menglu Li, Rasha Kashef, Ahmed Ibrahim
doaj   +1 more source

A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method

Information, 2019
Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information.
Liying Wang, Lei Shi, Liancheng Xu, Peiyu Liu, Lindong Zhang, Yanru Dong   +5 more
doaj   +1 more source

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

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

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

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

Speedup Two-Class Supervised Outlier Detection

IEEE Access, 2018
Outlier detection is an important topic in the community of data mining and machine learning. In two-class supervised outlier detection, it needs to solve a large quadratic programming whose size is twice the number of samples in the training set.
Yugen Yi, Wei Zhou, Yanjiao Shi, Jiangyan Dai   +3 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.
Shah, Nidhi, Altschul, Stephen F., Pop, Mihai   +2 more
openaire   +6 more sources