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A Practical Heterogeneous Classifier for Relational Databases

8 years 9 months ago
A Practical Heterogeneous Classifier for Relational Databases
Most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a "flat" form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a twophase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. A preliminary evaluation on TPCH and UCI benchmarks shows reduced training time without any loss of prediction accuracy.
Geetha Manjunath, M. Narasimha Murty, Dinkar Sitar
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICPR
Authors Geetha Manjunath, M. Narasimha Murty, Dinkar Sitaram
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