Classifying real-world data with the \(DD\alpha\)-procedure

DOI10.1007/s11634-014-0180-8zbMath1414.62258arXiv1407.5185OpenAlexW2083530143MaRDI QIDQ2418399

Pavlo Mozharovskyi, Tatjana Lange, Karl C. Mosler

Publication date: 3 June 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1407.5185

zbMATH Keywords

classificationfeaturessupervised learningdata depthalpha-procedureprojection depthspatial depthrandom Tukey depthoutsiders

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04) Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (11)

β-Skeleton depth functions and medians ⋮ Approximate computation of projection depths ⋮ Choosing among notions of multivariate depth statistics ⋮ A weighted localization of halfspace depth and its properties ⋮ Multivariate tests of uniformity ⋮ Fast \(DD\)-classification of functional data ⋮ T3C: improving a decision tree classification algorithm's interval splits on continuous attributes ⋮ A generalized spatial sign covariance matrix ⋮ Classification with the pot-pot plot ⋮ Uniform convergence rates for the approximated halfspace and projection depth ⋮ RR-classifier: a nonparametric classification procedure in multidimensional space based on relative ranks

Uses Software

• R
• UCI-ml
• SVMlight
• ElemStatLearn
• ddalpha

Cites Work

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• Fast nonparametric classification based on data depth
• The reduction principle in problems of revealing regularities. I
• Multivariate dispersion, central regions and depth. The lift zonoid approach
• Computing projection depth and its associated estimators
• Fast and robust discriminant analysis
• The random Tukey depth
• Zonoid trimming for multivariate distributions
• General notions of statistical depth function.
• Comparison between various regression depth methods and the support vector machine to approximate the minimum number of misclassifications
• Support-vector networks
• Nonparametrically consistent depth-based classifiers
• Data depths satisfying the projection property
• Multisurface method of pattern separation for medical diagnosis applied to breast cytology.
• The multivariate L ₁ -median and associated data depth
• DD-Classifier: Nonparametric Classification Procedure Based onDD-Plot
• Computing Halfspace Depth and Regression Depth
• Measuring overlap in binary regression.

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