Fuzzy entropy and conditioning
Information Sciences: an International Journal
Quotients with respect to similarity relations
Fuzzy Sets and Systems - Mathematics and Fuzziness, Part 1
Multivariate statistics: a practical approach
Multivariate statistics: a practical approach
C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
A view on rough set concept approximations
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
The evaluation of evidence for exponentially distributed data
Computational Statistics & Data Analysis
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
Fuzzy-rough approaches for mammographic risk analysis
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
Impact analysis of Jensen and Sk pal fuzzification in classification
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
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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.