Identifying uncertain galaxy morphologies using unsupervised learning

Edwards, Kieran (2013) Identifying uncertain galaxy morphologies using unsupervised learning. BSc dissertation, University of Portsmouth.

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    Abstract

    With the onset of massive cosmological data collection through mediums such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as “Uncertain”. This has motivated the conduct of experiments with data obtained from the SDSS database using each galaxy’s centre point right ascension and centre point declination values, together with the Galaxy Zoo morphology class label and the K-Means clustering algorithm. This project identifies the best attributes for clustering, using a novel heuristic algorithm called Incremental Feature Selection (IFS), and applies an unsupervised learning technique in order to improve the classification of galaxies labelled as “Uncertain” and increase the overall accuracies of such data clustering processes. Through the use of the IFS algorithm, it is observed that the accuracy of classes-to-clusters evaluation, by selecting the best combination of attributes via information gain, is further increased by approximately 15-20%. An accuracy of 82.627% was also achieved after conducting various experiments on the galaxies labelled as “Uncertain” and replacing them back into the original data set. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants. This project also features a paper accepted into a conference, describing the promising results and successful implementation of the novel heuristic algorithm.

    Item Type: Dissertation
    Departments/Research Groups: Faculty of Technology > School of Computing
    Depositing User: Alice Bentley
    Date Deposited: 12 Sep 2013 09:16
    Last Modified: 28 Jan 2015 12:28
    URI: http://eprints.port.ac.uk/id/eprint/13274

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