Applications V: Forensic Glass Analysis


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. This chapter presents the investigation into the use of feature evaluation for the purpose of selecting a single variable to model 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.



R. Jensen and Q. Shen. Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. IEEE Press/Wiley & Sons.