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Relational Classification using Multiple View Approach with Voting

by Shraddha Modi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Number 16
Year of Publication: 2013
Authors: Shraddha Modi
10.5120/12153-8126

Shraddha Modi . Relational Classification using Multiple View Approach with Voting. International Journal of Computer Applications. 70, 16 ( May 2013), 31-36. DOI=10.5120/12153-8126

@article{ 10.5120/12153-8126,
author = { Shraddha Modi },
title = { Relational Classification using Multiple View Approach with Voting },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 70 },
number = { 16 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume70/number16/12153-8126/ },
doi = { 10.5120/12153-8126 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:33:03.134186+05:30
%A Shraddha Modi
%T Relational Classification using Multiple View Approach with Voting
%J International Journal of Computer Applications
%@ 0975-8887
%V 70
%N 16
%P 31-36
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Classification is an important task in data mining and machine learning, in which a model is generated based on training dataset and that model is used to predict class label of unknown dataset. Various algorithms have been proposed to build accurate and scalable classifiers in data mining. These algorithms are only applied to single table. Today most real-world data are stored in relational format which is popular format for structured data which consist of tables connected via relations (primary key/ foreign key). So single table data mining algorithms cannot deal with relational data. To classify data from relational format need of multirelational classification arise which is used to analyze relational data and used to predict behaviour and unknown pattern automatically. For multirelational classification, various techniques are available which include upgrading existing algorithm, flatten relational data and multiple view approach. Multiple view approach learns from multiple views of a relational data and then combines the result of each view to classify unknown data. This paper presents proposed algorithm and experimental results for multiple view approach with voting as a view combination technique.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Inductive logic programming Multi relational classification Multiple view Multi-view Relational database

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