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While many techniques exist to classify data possessing straightforward characteristics, they tend to fail when dealing with the ldquocurse of dimensionalityrdquo. This condition, in which the ratio of features to samples is very large, is prevalent in many complex, voluminous biomedical datasets acquired using current spectroscopic modalities. We present a novel classification method using an adaptive...
In this paper, a multi-objective genetic algorithm for data clustering based on the robust fuzzy least trimmed squares estimator is proposed. The clustering methodology addresses two critical issues in unsupervised data clustering - the ability to produce meaningful classification in noisy data, and the requirement that the number of clusters be known a priori. The GA-driven clustering routine optimizes...
The fuzzy methods for determining associations between variables that have been proposed for use in the social sciences (see Ragin 2000, Smithson and Verkulien 2006) are measures of subsethood. Using these measures with social science data has proven to be problematic. This paper examines the use of fuzzy subsethood and fuzzy equality operators as tools for assessing factors hpothesized to be associated...
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