Detecting multiple outliers in regression data using genetic algorithms

Crawford, Kelly D.; Vasicek, Daniel J.; Wainwright, Roger L.

doi:10.1145/315891.316017

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Another kind of outlier detection is deviation-based. Crawford et al (1995) detect outliers using genetic algorithms, which is an optimization technique based on various biological principles. This approach is capable of searching for global optima among a vast number of choices.…”

Section: Methodsmentioning

confidence: 99%

Outlier detection in astronomical data

2004

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Astronomical data sets have experienced an unprecedented and continuing growth in the volume, quality, and complexity over the past few years, driven by the advances in telescope, detector, and computer technology. Like many other fields, astronomy has become a very data rich science. Information content measured in multiple Terabytes, and even larger, multi Petabyte data sets are on the horizon. To cope with this data flood, Virtual Observatory (VO) federates data archives and services representing a new information infrastructure for astronomy of the 21st century and provides the platform to science discovery. Data mining promises to both make the scientific utilization of these data sets more effective and more complete, and to open completely new avenues of astronomical research. Technological problems range from the issues of database design and federation, to data mining and advanced visualization, leading to a new toolkit for astronomical research. This is similar to challenges encountered in other data intensive fields today. Outlier detection is of great importance, as one of four knowledge discovery tasks. The identification of outliers can often lead to the discovery of truly unexpected knowledge in various fields. Especially in astronomy, the great interest of astronomers is to discover unusual, rare or unknown types of astronomical objects or phenomena. The outlier detection approaches in large datasets correctly meet the need of astronomers. In this paper we provide an overview of some techniques for automated identification of outliers in multivariate data. Outliers often provide useful information. Their identification is important not only for improving the analysis but also for indicating anomalies which may require further investigation. The technique may be used in the process of data preprocessing and also be used for preselecting special object candidates.

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Section: Methodsmentioning

confidence: 99%

Outlier detection in astronomical data

2004

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“…Another kind of outlier detection is deviation-based. Crawford et al (1995) 33 detect outliers using genetic algorithms, which is an optimization technique based on various biological principles. This approach is capable of searching for global optima among a vast number of choices.…”

Section: The System Of Multiwavelength Data Miningmentioning

confidence: 99%

System architectural design of multiwavelength data mining

2008

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Data avalanche faced in astronomy, astronomical data covers from radio, infrared, optical, X-ray, even gamma ray band. Astronomy enters an all sky-survey era. Transforming data into knowledge depends on data mining techniques. How to effectively and efficiently extract knowledge from databases is an important issue. Especially mining knowledge from different bands or multiband is of great significance. In this paper, we design a system which includes four fundamental blocks: the first is used to create databases; the second for cross-matching objects from different bands, the third for mining knowledge from the large data volume and the last one for final result evaluation. The functionalities of the four blocks are described. The cross-match results are divided, and the analysis mode for each of them is touched upon. Moreover the schemes of classification, regression, clustering analysis and outlier detection are demonstrated.

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Diagnostik von Regressionsschätzungen bei kleinen Stichproben (mit einem Exkurs zu logistischer Regression)

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Detecting multiple outliers in regression data using genetic algorithms

Cited by 4 publications

References 8 publications

Outlier detection in astronomical data

Outlier detection in astronomical data

System architectural design of multiwavelength data mining

Diagnostik von Regressionsschätzungen bei kleinen Stichproben (mit einem Exkurs zu logistischer Regression)

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