An immune programming-based ranking function discovery approach for effective information retrieval

Shuaiqiang Wang; Jun Ma; Qiang He

doi:10.1016/j.eswa.2010.02.019

ScienceGate Book Chapters

JOURNAL ARTICLE

An immune programming-based ranking function discovery approach for effective information retrieval

Shuaiqiang Wang Jun Ma Qiang He

Year: 2010 Journal: Expert Systems with Applications Vol: 37 (8)Pages: 5863-5871 Publisher: Elsevier BV

DOI: 10.1016/j.eswa.2010.02.019

Get Full-Text PDF Get Analytical Report

Keywords:

Learning to rank Computer science Ranking (information retrieval) Machine learning Rank (graph theory) Representation (politics) Ranking SVM Artificial intelligence Convergence (economics) Function (biology) Genetic programming Mathematics

Metrics

Cited By

0.21

FWCI (Field Weighted Citation Impact)

Refs

0.56

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Artificial Immune Systems Applications

Physical Sciences → Engineering → Biomedical Engineering

HIV Research and Treatment

Life Sciences → Immunology and Microbiology → Virology

Metaheuristic Optimization Algorithms Research

Physical Sciences → Computer Science → Artificial Intelligence

An immune programming-based ranking function discovery approach for effective information retrieval

Metrics

Citation History

Topics

Related Documents

A generic ranking function discovery framework by genetic programming for information retrieval

Discovery of context-specific ranking functions for effective information retrieval using genetic programming

Fuzzy Based Approach to Develop Hybrid Ranking Function for Efficient Information Retrieval

Genetic-based approaches in ranking function discovery and optimization in information retrieval — A framework

Nonlinear ranking function representations in genetic programming-based ranking discovery for personalized search