Various forms of indexing HDMR for modelling multivariate classification problems

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets t...

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Bibliographic Details
Main Authors: Aksu Çağrı, Alper, Tunga M
Format: Conference Proceeding
Language:English
Subjects:
ACCURACY
ANALYTICAL SOLUTION
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
CLASSIFICATION
COMPUTERIZED SIMULATION
Data structures
Datasets
INDEXES
Indexing
INTERPOLATION
Machine learning
Modelling
MULTIVARIATE ANALYSIS
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Summary:The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4904560