Work place: Department of Computer Science and Engineering, University of Dhaka, Dhaka-1000, Bangladesh
E-mail: sh.sayem.haque36@gmail.com
Website:
Research Interests: Data Science
Biography
Mohd. Sayemul Haque attained his Bachelor of Science degree in Computer Science from the University of Dhaka. Presently, he holds the position of Senior Software Engineer at Enosis Solutions, Bangladesh. His research interest lies in Data Science and the learning-to-rank domain. In addition to his involvement in various challenging projects, he has made significant contributions to the learning-to-rank domain through his role as the primary author in a journal publication.
By Mohd. Sayemul Haque Md. Fahim Muhammad Ibrahim
DOI: https://doi.org/10.5815/ijisa.2024.04.06, Pub. Date: 8 Aug. 2024
Learning-to-rank is an applied domain of supervised machine learning. As feature selection has been found to be effective for improving the accuracy of learning models in general, it is intriguing to investigate this process for learning-to-rank domain. In this study, we investigate the use of a popular meta-heuristic approach called simulated annealing for this task. Under the general framework of simulated annealing, we explore various neighborhood selection strategies and temperature cooling schemes. We further introduce a new hyper-parameter called the progress parameter that can effectively be used to traverse the search space. Our algorithms are evaluated on five publicly benchmark datasets of learning-to-rank. For a better validation, we also compare the simulated annealing-based feature selection algorithm with another effective meta-heuristic algorithm, namely local beam search. Extensive experimental results show the efficacy of our proposed models.
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