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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Feature Selection for Learning-to-Rank using Simulated Annealing

Author 1: Mustafa Wasif Allvi
Author 2: Mahamudul Hasan
Author 3: Lazim Rayan
Author 4: Mohammad Shahabuddin
Author 5: Md. Mosaddek Khan
Author 6: Muhammad Ibrahim

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110387

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 3, 2020.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Machine learning is being applied to almost all corners of our society today. The inherent power of large amount of empirical data coupled with smart statistical techniques makes it a perfect choice for almost all prediction tasks of human life. Information retrieval is a discipline that deals with fetching useful information from a large number of documents. Given that today millions, even billions, of digital documents are available, it is no surprise that machine learning can be tailored to this task. The task of learning-to-rank has thus emerged as a well-studied domain where the system retrieves the relevant documents from a document corpus with respect to a given query. To be successful in this retrieving task, machine learning models need a highly useful set of features. To this end, meta-heuristic optimization algorithms may be utilized. The aim of this work is to investigate the applicability of a notable meta-heuristic algorithm called simulated annealing to select an effective subset of features from the feature pool. To be precise, we apply simulated annealing algorithm on the well-known learning-to-rank datasets to methodically select the best subset of features. Our empirical results show that the proposed framework achieve gain in accuracy while using a smaller subset of features, thereby reducing training time and increasing effectiveness of learning-to-rank algorithms.

Keywords: Information retrieval; learning-to-rank; feature se-lection; meta-heuristic optimization algorithm; simulated annealing

Mustafa Wasif Allvi, Mahamudul Hasan, Lazim Rayan, Mohammad Shahabuddin, Md. Mosaddek Khan and Muhammad Ibrahim, “Feature Selection for Learning-to-Rank using Simulated Annealing” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110387

@article{Allvi2020,
title = {Feature Selection for Learning-to-Rank using Simulated Annealing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110387},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110387},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
author = {Mustafa Wasif Allvi and Mahamudul Hasan and Lazim Rayan and Mohammad Shahabuddin and Md. Mosaddek Khan and Muhammad Ibrahim}
}


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