Results 1 to 10 of about 258,865 (274)
Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy. [PDF]
VirusesJacob Khoury S +3 more
europepmc +1 more source
Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images. [PDF]
BMC Med Inform Decis MakSelle M +4 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, 2019
Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang +2 more
+4 more sources
Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang +2 more
+4 more sources
ACM Computing Surveys, 2020
Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments, among other factors.
Azzedine Boukerche +2 more
openaire +1 more source
Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments, among other factors.
Azzedine Boukerche +2 more
openaire +1 more source
WIREs Data Mining and Knowledge Discovery, 2011
AbstractOutlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years ...
Xiaogang Su, Chih‐Ling Tsai
openaire +2 more sources
AbstractOutlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years ...
Xiaogang Su, Chih‐Ling Tsai
openaire +2 more sources
2018
This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Economics and Finance. Please check back later for the full article. Detection of outliers is an important explorative step in empirical analysis.
openaire +2 more sources
This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Economics and Finance. Please check back later for the full article. Detection of outliers is an important explorative step in empirical analysis.
openaire +2 more sources