File(s) not publicly available

Optimization of parameters for effective web information retrieval using an evolutionary algorithm

conference contribution
posted on 06.12.2017, 00:00 by J Zakos, P Zhang, Brijesh Verma
In this paper we present an approach based on the application of an evolutionary algorithm to optimally tune the parameters of a novel technique for effective web information retrieval. Context matching is a context-based technique for the ad-hoc retrieval of web documents that relies on a number of inter-related parameters that define the nature of the context it uses. Its aim is to dynamically generate a context-based measure of term significance during retrieval that can be used as an indicator of document relevancy and ultimately contribute to a documents rank score. But the optimal setting of context matching parameters is an important aspect of the technique to ensure effective retrieval. Thus, the goal of this paper is to investigate the use of an evolutionary algorithm for the optimization of context matching parameters and compare its performance to an iterative technique that exhaustively explores combinations of parameters. We show how the most effective settings for parameters are obtained efficiently through the evolutionary algorithm. We also show how context matching, through the use of these optimized parameters, achieves effective retrieval results on benchmark data that are a significant improvement on previously published results.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

582

End Page

587

Number of Pages

6

Start Date

01/01/2005

ISBN-10

0780390490

Location

Montreal, Canada

Publisher

IEEE

Place of Publication

New Jersey, USA.

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

IEEE International Joint Conference on Neural Networks

Exports

CQUniversity

Exports