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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Learning a ranking function for information retrieval using HybridABC

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    10201028.pdf (1.305Mb)
    Date
    2015-12
    Publisher
    BRAC University
    Author
    Newaz, S.M Saif
    Pieta, Maliha Anjum
    Ahmed, Mehreen
    Metadata
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    URI
    http://hdl.handle.net/10361/4892
    Abstract
    In this paper we propose a ranking algorithm, HybridABC that is built on swarm based algorithm. In our proposed HybridABC algorithm we merged Artificial Bee Colony (ABC) algorithm with Differential Evolution (DE) algorithm. The ABC is a swarm-based metaheuristic algorithm inspired by the intelligent foraging pattern of bees and Differential Evolution is a population-based stochastic search technique. The proposed implementation of ABC has been tested using the LETOR dataset, which is a standard benchmark dataset for evaluating ranking functions. Our results display that our proposed HybridABC can compete and in many cases more efficient than other state-of-the-art algorithm proposed in ranking web pages based on Genetic Algorithm (GA).
    Keywords
    Computer science and engineering; HybridABC
     
    Description
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.
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    • Thesis & Report, BSc (Computer Science and Engineering)

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