Feature Selection for Aiding Glass Forensic Evidence Analysis

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dc.contributor.author Shen, Qiang
dc.contributor.author Jensen, Richard
dc.date.accessioned 2009-09-14T10:08:06Z
dc.date.available 2009-09-14T10:08:06Z
dc.date.issued 2009
dc.identifier.citation Shen , Q & Jensen , R 2009 , ' Feature Selection for Aiding Glass Forensic Evidence Analysis ' Intelligent Data Analysis , vol 13 , no. 5 , pp. 703-723 . en
dc.identifier.issn 1088-467X
dc.identifier.other PURE: 124332
dc.identifier.other dspace: 2160/3039
dc.identifier.uri http://hdl.handle.net/2160/3039
dc.identifier.uri http://www.iospress.nl/loadtop/load.php?isbn=1088467x en
dc.description R. Jensen and Q. Shen. Feature Selection for Aiding Glass Forensic Evidence Analysis. Intelligent Data Analysis, vol. 13, no. 5, pp. 703-723, 2009. en
dc.description.abstract The evaluation of glass evidence in forensic science is an important issue. Traditionally, this has depended on the comparison of the physical and chemical attributes of an unknown fragment with a control fragment. A high degree of discrimination between glass fragments is now achievable due to advances in analytical capabilities. A random effects model using two levels of hierarchical nesting is applied to the calculation of a likelihood ratio (LR) as a solution to the problem of comparison between two sets of replicated continuous observations where it is unknown whether the sets of measurements shared a common origin. Replicate measurements from a population of such measurements allow the calculation of both within-group and between-group variances. Univariate normal kernel estimation procedures have been used for this, where the between-group distribution is considered to be non-normal. However, the choice of variable for use in LR estimation is critical to the quality of LR produced. This paper investigates the use of feature selection for the purpose of selecting the variable for estimation without the need for expert knowledge. Results are recorded for several selectors using normal, exponential, adaptive and biweight kernel estimation techniques. Misclassification rates for the LR estimators are used to measure performance. The experiments performed reveal the capability of the proposed approach for this task. en
dc.format.extent 21 en
dc.language.iso eng
dc.relation.ispartof Intelligent Data Analysis en
dc.subject Feature selection en
dc.subject fuzzy-rough sets en
dc.subject glass analysis en
dc.subject forensic evidence en
dc.subject two-level model en
dc.title Feature Selection for Aiding Glass Forensic Evidence Analysis en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.3233/IDA-2009-0389
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en


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