CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Relational Classification using Multiple View Approach with Voting

by Shraddha Modi
journal cover thumbnail

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
  1. Hongyu Guo, Herna L. Viktor, "Multirelational Classification: A Multiple View Approach", ACM, KNOWLEDGE AND INFORMATION SYSTEMS, Volume 17, Number 3, 287-312, DOI: 10. 1007/ s10115-008-0127-5, 2008
  2. Raymond J. Mooney, Prem Melville, Lappoon Rupert Tang, "Relational Data Mining with Inductive Logic Programming for Link Discovery", Appears in the Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Nov. 2002
  3. Dr. M. Thangaraj, C. R. Vijayalakshmi, "A Study on Classification Approaches across Multiple Database Relations", International Journal of Computer Applications (0975 – 8887), Volume 12– No. 12, DOI: 10. 5120/1740-2366, January 2011
  4. Blockeel, H. , Top-down induction of first order logical decision trees, Artificial Intelligence Journal, vol. 101,pp. 285-297, 1998
  5. Kramer S. , Widmer G. , "Inducing Classification and Regression Tress in First Order Logic", Relational Data Mining, pp. 140-159, Springer, 2001
  6. Yin X, Han J, Yu PS, "CrossMine: Efficient Classification across Multiple Database Relations". In Proceedings of 20th Int. Conf. on Data Engineering (ICDE'04), 2004
  7. Hector Ariel Leiva, Shashi Gadia, Drena Dobbs, "MRDTL: A multi-relational decision tree learning algorithm", Proceedings of the 13th International Conference on Inductive Logic Programming, 2002
  8. Atramentov A. , Leiva H. , Honavar V. , "A Multirelational Decision Tree Learning Algorithm - Implementation and Experiments", ILP LNCS, Vol. 2835, pp. 38-56, 2003
  9. Lise Getoor, Nir Friedman, Daphne Koller, Avi Pfeffer, Ben Taskar, "Probabilistic Relational Models", In An Introduction to Statistical Relational Learning, August 2007
  10. Liu H. , Yin X. ,Han J. , "A Efficient Multirelational Naïve Bayesian Classifier Based on Semantic Relationship Graph", In MRDM'05 Proceedings of 4th international workshop on MRDM, 2005
  11. X. Yan, J. Han, "gSpan: Graph-based substructure pattern mining". In Proc. 2002 Int. Conf. on Data Mining (ICDM'02), Maebashi, Japan, Dec. 2002.
  12. Dehaspe L. , Raedt D. , "Mining Association Rules in Multiple Relations", In Proceedings of the ILP, Springer- Verlang, London UK, pp. 125-132, 1997
  13. Yingqin Gu, Hongyan Liu, Jun He, Bo Hu, Xiaoyong Du, "MrCAR: A Multi-relational Classification Algorithm Based on Association Rules", IEEE, Web Information Systems and Mining, 2009. WISM 2009. International Conference, 31 December 2009
  14. Francisco Moreno-Seco; Jose M. Inesta; Pedro J. Ponce de Leon; Luisa Mico, "Comparison of classi?er fusion methods for Classi?cation in pattern recognition tasks", 20
Index Terms

Computer Science
Information Sciences

Keywords

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

Powered by PhDFocusTM