Results 11 to 20 of about 42,043 (166)
Nearest Centroid Classifier with Outlier Removal for Classification
JITeCS (Journal of Information Technology and Computer Science), 2020 Classification method is misled by outlier. However, there are few research of classification with outlier removal, especially for Nearest Centroid Classifier Method. The proposed methodology consists of two stages.
Aditya Hari Bawono +2 more
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Normality testing after outlier removal
Econometrics and Statistics, 2023 The cumulant based normality test after outlier removal is analyzed. It is shown that the standard least squares normalizations can be misleading in this context. The sample cumulants should be standardized according to the truncation imposed at the removal stage and the estimation method being used.
Berenguer-Rico, V, Nielsen, B
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IEEE Access, 2022 The software engineering researchers have worked on different dimensions to facilitate better software effort estimates, including those focusing on dataset quality improvement.
Swarnima Singh Gautam, Vrijendra Singh
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An adaptive outlier removal aided k-means clustering algorithm
Journal of King Saud University: Computer and Information Sciences, 2022 K-means is one of ten popular clustering algorithms. However, k-means performs poorly due to the presence of outliers in real datasets. Besides, a different distance metric makes a variation in data clustering accuracy. Improve the clustering accuracy of
Nawaf H.M.M. Shrifan +2 more
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A Geometric Estimation Technique Based on Adaptive M-Estimators: Algorithm and Applications
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Robust fitting is a basic technique and has been widely applied in photogrammetry and remote sensing, such as geometric correction. As known, typical robust estimators (include M-estimators, S-estimators, MM-estimators, etc.) often fail when outlier rate
Jiayuan Li +3 more
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Outliers May Not Be Automatically Removed
Journal of Experimental Psychology: General, 2022 Researchers often remove outliers when comparing groups. It is well documented that the common practice of removing outliers within groups leads to inflated type I error rates. However, it was recently argued by André that if outliers are instead removed across groups, type I error rates are not inflated. The same study discusses that removing outliers
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SPATIAL ANALYSIS FOR OUTLIER REMOVAL FROM LIDAR DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 Outlier detection in LiDAR point clouds is a necessary process before the subsequent modelling. So far, many studies have been done in order to remove the outliers from LiDAR data.
A. A. Matkan +4 more
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GraphSLAM Improved by Removing Measurement Outliers [PDF]
Journal of Korean Institute of Intelligent Systems, 2011 This paper presents the GraphSLAM improved by selecting the measurement with respect to their likelihoods. GraphSLAM estimates the robot`s path and map by utilizing the entire history of input data. However, GraphSLAM`s performance suffers a lot from severely noisy measurements.
Ryun-Seok Kim +2 more
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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 +2 more
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SUPERVISED OUTLIER DETECTION IN LARGE-SCALE MVS POINT CLOUDS FOR 3D CITY MODELING APPLICATIONS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 We propose to use a discriminative classifier for outlier detection in large-scale point clouds of cities generated via multi-view stereo (MVS) from densely acquired images.
C. Stucker +3 more
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