Digital Commons Network^™

Predictive Validity And Classification Accuracy Of Actigraph Energy Expenditure Equations And Cut-Points In Young Children, Xanne Janssen, Dylan P. Cliff, John J. Reilly, Trina Hinkley, Rachel A. Jones, Marijka Batterham, Ulf Ekelund, Soren Brage, Anthony D. Okely

Dr Marijka Batterham

Objectives Evaluate the predictive validity of ActiGraph energy expenditure equations and the classification accuracy of physical activity intensity cut-points in preschoolers. Methods Forty children aged 4–6 years (5.3±1.0 years) completed a ~150-min room calorimeter protocol involving age-appropriate sedentary, light and moderate-to vigorous-intensity physical activities. Children wore an ActiGraph GT3X on the right mid-axillary line of the hip. Energy expenditure measured by room calorimetry and physical activity intensity classified using direct observation were the criterion methods. Energy expenditure was predicted using Pate and Puyau equations. Physical activity intensity was classified using Evenson, Sirard, Van Cauwenberghe, Pate, Puyau, and Reilly, ActiGraph cut-points. …

Stretching Conceptual Structures In Classifications Across Languages And Cultures., Barbara H. Kwasnik, Victoria L. Rubin

Victoria Rubin

The authors describe the difficulties of translating classifications from a source language and culture to another language and culture. To demonstrate these problems, kinship terms and concepts from native speakers of fourteen languages were collected and analyzed to find differences between their terms and structures and those used in English. Using the representations of kinship terms in the Library of Congress Classification (LCC) and the Dewey Decimal Classification (DDC) as examples, the authors identified the source of possible lack of mapping between the domain of kinship in the fourteen languages studied and the LCC and DDC. Finally, some preliminary suggestions …

Rough-Fuzzy Hybrid Approach For Identification Of Bio-Markers And Classification On Alzheimer's Disease Data, Changsu Lee, Chiou-Peng Lam, Martin Masek

Martin Masek

A new approach is proposed in this paper for identification of biomarkers and classification on Alzheimer's disease data by employing a rough-fuzzy hybrid approach called ARFIS (a framework for Adaptive TS-type Rough-Fuzzy Inference Systems). In this approach, the entropy-based discretization technique is employed first on the training data to generate clusters for each attribute with respect to the output information. The rough set-based feature reduction method is then utilized to reduce the number of features in a decision table obtained using the cluster information. Another rough set-based approach is employed for the generation of decision rules. After the construction and …

Human Performance Engineering Approach, Dotan I. Shvorin

Dr. Dotan Shvorin

Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao

Zhongmei Yao

Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …