Feature selection strategies for classifying high dimensional astronomical data sets

Ciro Donalek, S. G. Djorgovski, Ashish A. Mahabal, Matthew J. Graham, Andrew J. Drake, A. Arun Kumar, N. Sajeeth Philip, Thomas J. Fuchs, Michael J. Turmon, Michael Ting Chang Yang, Giuseppe Longo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013
PublisherIEEE Computer Society
Pages35-41
Number of pages7
ISBN (Print)9781479912926
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 IEEE International Conference on Big Data, Big Data 2013 - Santa Clara, CA, United States
Duration: 6 Oct 20139 Oct 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Big Data, Big Data 2013

Conference

Conference2013 IEEE International Conference on Big Data, Big Data 2013
Country/TerritoryUnited States
CitySanta Clara, CA
Period6/10/139/10/13

Keywords

  • CRTS
  • astroinformatics
  • feature selection
  • machine learning

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