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Actual Differences (actual + difference)
Selected AbstractsEvaluation Metrics in Classification: A Quantification of Distance-BiasCOMPUTATIONAL INTELLIGENCE, Issue 3 2003Ricardo Vilalta This article provides a characterization of bias for evaluation metrics in classification (e.g., Information Gain, Gini, ,2, etc.). Our characterization provides a uniform representation for all traditional evaluation metrics. Such representation leads naturally to a measure for the distance between the bias of two evaluation metrics. We give a practical value to our measure by observing the distance between the bias of two evaluation metrics and its correlation with differences in predictive accuracy when we compare two versions of the same learning algorithm that differ in the evaluation metric only. Experiments on real-world domains show how the expectations on accuracy differences generated by the distance-bias measure correlate with actual differences when the learning algorithm is simple (e.g., search for the best single feature or the best single rule). The correlation, however, weakens with more complex algorithms (e.g., learning decision trees). Our results show how interaction among learning components is a key factor to understand learning performance. [source] Commentary: A Response to Reckase's Conceptual Framework and Examples for Evaluating Standard Setting MethodsEDUCATIONAL MEASUREMENT: ISSUES AND PRACTICE, Issue 3 2006E. Matthew Schulz A look at real data shows that Reckase's psychometric theory for standard setting is not applicable to bookmark and that his simulations cannot explain actual differences between methods. It is suggested that exclusively test-centered, criterion-referenced approaches are too idealized and that a psychophysics paradigm and a theory of group behavior could be more useful in thinking about the standard setting process. In this view, item mapping methods such as bookmark are reasonable adaptations to fundamental limitations in human judgments of item difficulty. They make item ratings unnecessary and have unique potential for integrating external validity data and student performance data more fully into the standard setting process. [source] Using Distraction to Reduce Reported Pain, Fear, and Behavioral Distress in Children and Adolescents: A Multisite StudyJOURNAL FOR SPECIALISTS IN PEDIATRIC NURSING, Issue 2 2000Karen L. Carlson ISSUES AND PURPOSE. Distraction during painful procedures has been shown to be effective in previous studies, yet this simple intervention is not used routinely. This study examined the effectiveness and feasibility of distraction in reducing behavioral distress, pain, and fear during venipuncture or intravenous insertion. DESIGN AND METHODS. A two-group randomized design with 384 children in 13 children's hospitals. RESULTS. Age was a significant factor in observed behavioral distress, reports of fear, and self-reported pain. The use of a kaleidoscope, however, did not significantly reduce pain or distress during venipuncture or IV insertion. PRACTICE IMPLICATIONS. Failure of the distraction intervention to reach statistical significance in this study is puzzling, given anecdotal reports of clinical efficacy. Methodological issues may have obscured actual differences between experimental and control groups. [source] Encoding and reconstruction in parallel MRINMR IN BIOMEDICINE, Issue 3 2006Klaas P. Pruessmann Abstract The advent of parallel MRI over recent years has prompted a variety of concepts and techniques for performing parallel imaging. A main distinguishing feature among these is the specific way of posing and solving the problem of image reconstruction from undersampled multiple-coil data. The clearest distinction in this respect is that between k -space and image-domain methods. The present paper reviews the basic reconstruction approaches, aiming to emphasize common principles along with actual differences. To this end the treatment starts with an elaboration of the encoding mechanisms and sampling strategies that define the reconstruction task. Based on these considerations a formal framework is developed that permits the various methods to be viewed as different solutions of one common problem. Besides the distinction between k -space and image-domain approaches, special attention is given to the implications of general vs lattice sampling patterns. The paper closes with remarks concerning noise propagation and control in parallel imaging and an outlook upon key issues to be addressed in the future. Copyright © 2006 John Wiley & Sons, Ltd. [source] | ||||||||||