Combining Heterogeneous Classifiers for Relational Databases

Details Combining Heterogeneous Classifiers for Relational Databases Combining Heterogeneous Classifiers for Relational Databases

2012-01-13

arxiv.org/abs/1201.2925v2

Computer Science

Learning

Withdrawn - as that was a trial upload only. Non public information

Scientific paper

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 practical, two-phase 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. The proposed algorithm was evaluated on three diverse datasets, namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy.

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