Combining heterogeneous classifiers for relational databases

Manjunath, Geetha and Murty, Narasimha M and Sitaram, Dinkar (2013) Combining heterogeneous classifiers for relational databases. In: Pattern Recognition, 46 (1). pp. 317-324.

PDF pat_reg_46_317_2013.pdf - Published Version Restricted to Registered users only Download (835kB) | Request a copy

Abstract

Practical usage of machine learning is gaining strategic importance in enterprises looking for business intelligence. However, 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. (C) 2012 Elsevier Ltd. All rights reserved.

Item Type:	Journal Article
Publication:	Pattern Recognition
Publisher:	Elsevier Science
Additional Information:	Copyright of this article belongs to Elsevier Science.
Keywords:	Heterogeneous Classifier; RDF; Relational Data; RDBMS
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	15 Feb 2013 09:26
Last Modified:	15 Feb 2013 09:33
URI:	http://eprints.iisc.ac.in/id/eprint/45361

Actions (login required)

View Item