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Improving SDG Classification with Topic Models and Combinatorial Fusion.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Improving SDG Classification with Topic Models and Combinatorial Fusion./
Author:	Orazbek, Ilyas.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	41 p.
Notes:	Source: Masters Abstracts International, Volume: 83-06.
Contained By:	Masters Abstracts International83-06.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28718454
ISBN:	9798496506847

Improving SDG Classification with Topic Models and Combinatorial Fusion.
Orazbek, Ilyas.

Improving SDG Classification with Topic Models and Combinatorial Fusion. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 41 p.

Source: Masters Abstracts International, Volume: 83-06.

Thesis (M.S.)--Fordham University, 2021.

This item must not be sold to any third party vendors.

Combinatorial fusion analysis (CFA) is a machine learning and artificial intelligence (ML/AI) paradigm for combining multiple scoring systems using the rank-score characteristic (RSC) function and cognitive diversity (CD). In this work, CFA is used to combine two topic models A and B to improve the classification precision. Each of these two models measures how similar the contents of a publication (or a document) are to each of the 17 Sustainable Development Goals (SDGs) of the United Nations. Each of the individual models is characterized and analyzed using the RSC function. The classification results of models A and B and combined models from score and rank combinations are also evaluated. Classification precision is calculated using Pre k, k = 1, 3, 5, and 8 when compared to classification results obtained by human experts.

ISBN: 9798496506847Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Cognitive diversity

Improving SDG Classification with Topic Models and Combinatorial Fusion.
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500 $a Source: Masters Abstracts International, Volume: 83-06.
500 $a Advisor: Hsu, Frank;Zhang, Xiaolan.
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520 $a Combinatorial fusion analysis (CFA) is a machine learning and artificial intelligence (ML/AI) paradigm for combining multiple scoring systems using the rank-score characteristic (RSC) function and cognitive diversity (CD). In this work, CFA is used to combine two topic models A and B to improve the classification precision. Each of these two models measures how similar the contents of a publication (or a document) are to each of the 17 Sustainable Development Goals (SDGs) of the United Nations. Each of the individual models is characterized and analyzed using the RSC function. The classification results of models A and B and combined models from score and rank combinations are also evaluated. Classification precision is calculated using Pre k, k = 1, 3, 5, and 8 when compared to classification results obtained by human experts.
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653 $a Rank score function
653 $a Sustainable development goals
653 $a Topic modeling
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