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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(2): 233-236 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.309

An Architecture to Enhance Post Retrieval Document Relevancy Using Integrated Techniques

Vimala Balakrishnan and Kian Ahmadi
Abstract—This paper is a proposal based on an on-going work in which we attempt to take advantage of information retrieval (IR) and case-based reasoning (CBR) techniques combined with the aim to improve the document relevancy of a search result. The proposed architecture contains two main phases: first is the IR phase whereby relevance feedback (RF) is implemented on search results produced based on adjacency keyword algorithm. Second is the CBR phase which further improves the results based on the output from phase one. This paper presents an explanation on the proposed RF-CBR model. It is believed that the integration of these two popular techniques would result in an improved document relevancy.

Index Terms—Adjacency keywords, case-based reasoning, KNN algorithm, relevance feedback.

The authors are with the Faculty of Computer Science and Information Systems, University of Malaya, Kuala Lumpur, Malaysia (e-mail: vimala.balakrishnan@um.edu.my, kian_santa@yahoo.com).

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Cite:Vimala Balakrishnan and Kian Ahmadi, "An Architecture to Enhance Post Retrieval Document Relevancy Using Integrated Techniques," International Journal of Machine Learning and Computing vol. 3, no. 2, pp. 233-236, 2013.

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