Exploiting result diversification methods for feature selection in learning to rank

2014-01-01
Djafari Naini, Kaweh
Altıngövde, İsmail Sengör
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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In this paper, we adopt various greedy result diversification strategies to the problem of feature selection for learning to rank. Our experimental evaluations using several standard datasets reveal that such diversification methods are quite effective in identifying the feature subsets in comparison to the baselines from the literature.
Feature selection, Feature selection method, Relevance score, Feature selection problem, Modern portfolio theory
https://hdl.handle.net/11511/41907
Department of Computer Engineering, Conference / Seminar

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Citation Formats
K. Djafari Naini and İ. S. Altıngövde, “Exploiting result diversification methods for feature selection in learning to rank,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41907.
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