Weihs / Gaul Classification - the Ubiquitous Challenge

Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Dortmund, March 9-11, 2004
2005
ISBN: 978-3-540-28084-2
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 717 Seiten, eBook

Reihe: Studies in Classification, Data Analysis, and Knowledge Organization

The contributions in this volume represent the latest research results in the field of classification, clustering, and data analysis. Besides the theoretical analysis, papers focus on various application fields as archaeology, astronomy, bio-sciences, business, electronic data and web, finance and insurance, library science and linguistics, marketing, music science, and quality assurance.

Zielgruppe

Research

Autoren/Hrsg.

Weihs, Claus

Gaul, Wolfgang

Gaul, Wolfgang A.

Inhaltsverzeichnis

(Semi-) Plenary Presentations.- Classification and Data Mining in Musicology.- Bayesian Mixed Membership Models for Soft Clustering and Classification.- Predicting Protein Secondary Structure with Markov Models.- Milestones in the History of Data Visualization: A Case Study in Statistical Historiography.- Quantitative Text Typology: The Impact of Word Length.- Cluster Ensembles.- Bootstrap Confidence Intervals for Three-way Component Methods.- Organising the Knowledge Space for Software Components.- Multimedia Pattern Recognition in Soccer Video Using Time Intervals.- Quantitative Assessment of the Responsibility for the Disease Load in a Population.- Classification and Data Analysis.- Bootstrapping Latent Class Models.- Dimensionality of Random Subspaces.- Two-stage Classification with Automatic Feature Selection for an Industrial Application.- Bagging, Boosting and Ordinal Classification.- A Method for Visual Cluster Validation.- Empirical Comparison of Boosting Algorithms.- Iterative Majorization Approach to the Distance-based Discriminant Analysis.- An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables.- Expectation of Random Sets and the ‘Mean Values’ of Interval Data.- Experimental Design for Variable Selection in Data Bases.- KMC/EDAM: A New Approach for the Visualization of K-Means Clustering Results.- Clustering of Variables with Missing Data: Application to Preference Studies.- Binary On-line Classification Based on Temporally Integrated Information.- Different Subspace Classification.- Density Estimation and Visualization for Data Containing Clusters of Unknown Structure.- Hierarchical Mixture Models for Nested Data Structures.- Iterative Proportional Scaling Based on a Robust Start Estimator.- Exploring Multivariate Data Structures with Local Principal Curves.- A Three-way Multidimensional Scaling Approach to the Analysis of Judgments About Persons.- Discovering Temporal Knowledge in Multivariate Time Series.- A New Framework for Multidimensional Data Analysis.- External Analysis of Two-mode Three-way Asymmetric Multidimensional Scaling.- The Relevance Vector Machine Under Covariate Measurement Error.- Applications.- A Contribution to the History of Seriation in Archaeology.- Model-based Cluster Analysis of Roman Bricks and Tiles from Worms and Rheinzabern.- Astronomical Object Classification and Parameter Estimation with the Gaia Galactic Survey Satellite.- Design of Astronomical Filter Systems for Stellar Classification Using Evolutionary Algorithms.- Analyzing Microarray Data with the Generative Topographic Mapping Approach.- Test for a Change Point in Bernoulli Trials with Dependence.- Data Mining in Protein Binding Cavities.- Classification of In Vivo Magnetic Resonance Spectra.- Modifying Microarray Analysis Methods for Categorical Data — SAM and PAM for SNPs.- Improving the Identification of Differentially Expressed Genes in cDNA Microarray Experiments.- PhyNav: A Novel Approach to Reconstruct Large Phylogenies.- NewsRec, a Personal Recommendation System for News Websites.- Clustering of Large Document Sets with Restricted Random Walks on Usage Histories.- Fuzzy Two-mode Clustering vs. Collaborative Filtering.- Web Mining and Online Visibility.- Analysis of Recommender System Usage by Multidimensional Scaling.- On a Combination of Convex Risk Minimization Methods.- Credit Scoring Using Global and Local Statistical Models.- Informative Patterns for Credit Scoring: Support Vector Machines Preselect Data Subsets for Linear Discriminant Analysis.- Application of Support Vector Machines in a Life Assurance Environment.- Continuous Market Risk Budgeting in Financial Institutions.- Smooth Correlation Estimation with Application to Portfolio Credit Risk.- How Many Lexical-semantic Relations are Necessary?.- Automated Detection of Morphemes Using Distributional Measurements.- Classification of Author and/or Genre? The Impact of Word Length.- Some Historical Remarks on Library Classification — a Short Introduction to the Science of Library Classification.- Automatic Validation of Hierarchical Cluster Analysis with Application in Dialectometry.- Discovering the Senses of an Ambiguous Word by Clustering its Local Contexts.- Document Management and the Development of Information Spaces.- Stochastic Ranking and the Volatility “Croissant”: A Sensitivity Analysis of Economic Rankings.- Importance Assessment of Correlated Predictors in Business Cycles Classification.- Economic Freedom in the 25-Member European Union: Insights Using Classification Tools.- Intercultural Consumer Classifications in E-Commerce.- Reservation Price Estimation by Adaptive Conjoint Analysis.- Estimating Reservation Prices for Product Bundles Based on Paired Comparison Data.- Classification of Perceived Musical Intervals.- In Search of Variables Distinguishing Low and High Achievers in Music Sight Reading Task.- Automatic Feature Extraction from Large Time Series.- Identification of Musical Instruments by Means of the Hough-Transformation.- Support Vector Machines for Bass and Snare Drum Recognition.- Register Classification by Timbre.- Classification of Processes by the Lyapunov Exponent.- Desirability to Characterize Process Capability.- Application and Use of Multivariate Control Charts in a BTA Deep Hole Drilling Process.- Determination of Relevant Frequencies and Modeling Varying Amplitudes of Harmonic Processes.- Contest: Social Milieus in Dortmund.- to the Contest “Social Milieus in Dortmund”.- Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering.- Annealed ?-Means Clustering and Decision Trees.- Correspondence Clustering of Dortmund City Districts.

Leseproben

Quantitative Assessment of the Responsibility for the Disease Load in a Population (p. 109-110)

Wolfgang Uter and Olaf Gefeller

Department of Medical Informatics, Biometry and Epidemiology,
University of Erlangen Nuremberg, Germany

Abstract. The concept of attributable risk (AR), introduced more than 50 years ago, quantifies the proportion of cases diseased due to a certain exposure (risk) factor. While valid approaches to the estimation of crude or adjusted AR exist, a problem remains concerning the attribution of AR to each of a set of several exposure factors. Inspired by mathematical game theory, namely, the axioms of fairness and the Shapley value, introduced by Shapley in 1953, the concept of partial AR has been developed. The partial AR offers a unique solution for allocating shares of AR to a number of exposure factors of interest, as illustrated by data from the German G¨ottingen Risk, Incidence, and Prevalence Study (G.R.I.P.S.).


1 Introduction

Analytical epidemiological studies aim at providing quantitative information on the association between a certain exposure, or several exposures, and some disease outcome of interest. Usually, the disease etiology under study is multifactorial, so that several exposure factors have to be considered simultaneously. The effect of a particular exposure factor on the dichotomous disease variable is quantified by some measure of association, including the relative risk (RR) or the odds ratio (OR), which will be explained in the next section.

While these measures indicate by which factor the disease risk increases if a certain exposure factor is present in an individual, the concept of attributable risk (AR) addresses the impact of an exposure on the overall disease load in the population. This paper focusses on the AR, which can be informally introduced as the answer to the question, "what proportion of the observed cases of disease in the study population suffers from the disease due to the exposure of interest?". In providing this information the AR places the concept of RR commonly used in epidemiology in a public health perspective, namely by providing an answer also to the reciprocal question, "what proportion of cases of disease could - theoretically - be prevented if the exposure factor could be entirely removed by some adequate preventive action?". Since its introduction in 1953 (Levin (1953)), the concept of AR is increasingly being used by epidemiological researchers.

However, while the One of the diffculties in applying the concept of AR is the question of how to adequately estimate the AR associated with several exposure factors of interest, and not just one single exposure factor. The present paper briefly introduces the concept of sequential attributable risk (SAR) and then focusses on the partial attributable risk (PAR), following an axiomatic approach founded on game theory. For illustrative purposes, data from a German cohort study on risk factors for myocardial infarction are used. methodology of this invaluable epidemiological measure has constantly been extended to cover a variety of epidemiological situations, its practical use has not followed these advances satisfactorily (reviewed by Uter and Pfahlberg (1999)).