Results 61 to 70 of about 4,573,641 (310)
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 +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 +2 more
openaire +6 more sources
IEEE AccessThe modified Z-score (mZ-score) method has been used to detect outliers in time series received signal strength (RSS) observations. Its performance is dependent on the scale estimator used, and each has advantages and disadvantages over the others.
A. Yaro +3 more
semanticscholar +1 more source
An Efficient Density-Based Local Outlier Detection Approach for Scattered Data
IEEE Access, 2019 After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore
Shubin Su +6 more
doaj +1 more source
Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data
Metabolites, 2023 Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM).
Michiel Bongaerts +9 more
doaj +1 more source
Outlier detection from ETL Execution trace
, 2011 Extract, Transform, Load (ETL) is an integral part of Data Warehousing (DW) implementation. The commercial tools that are used for this purpose captures lot of execution trace in form of various log files with plethora of information.
Chakrabarti, Amlan +2 more
core +1 more source
Molecular Oncology, EarlyView.This study indicates that Merkel cell carcinoma (MCC) does not originate from Merkel cells, and identifies gene, protein & cellular expression of immune‐linked and neuroendocrine markers in primary and metastatic Merkel cell carcinoma (MCC) tumor samples, linked to Merkel cell polyomavirus (MCPyV) status, with enrichment of B‐cell and other immune cell
Richie Jeremian +10 more
wiley +1 more source
A Survey on Mixed-Attribute Outlier Detection Methods
CommIT Journal, 2019 In the data era, outlier detection methods play an important role. The existence of outliers can provide clues to the discovery of new things, irregularities in a system, or illegal intruders.
Nur Rokhman
doaj +1 more source
Contextual Outlier Interpretation
, 2018 Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective ...
Hu, Xia, Liu, Ninghao, Shin, Donghwa
core +1 more source
Online boxplot derived outlier detection
International Journal of Data Science and AnalysisOutlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers
Arefeh Mazarei +4 more
semanticscholar +1 more source