Dipolar Data Integration Through Univariate, Binary Classifiers
Aggregation of large data sets is one of the current topics of exploratory analysis and pattern recognition. Integration of data sets is a useful and necessary step towards knowledge extraction from large data sets. The possibility of separable integration of multidimensional data sets by one dimensional binary classifiers is analyzed in the paper, as well as designing a layer of binary classifiers for separable aggregation. The optimization problem of separable layer designing is formulated. A dipolar strategy aimed at optimizing separable aggregation of large data sets is proposed in the presented paper.
KeywordsData integration Separability Dipolar strategy Univariate binary classifiers Partially structured data
The presented study was supported by the grant S/WI/2/2013 from Bialystok University of Technology and funded from the resources for research by Polish Ministry of Science and Higher Education.
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