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Improving SDG Classification Precision Using Combinatorial Fusion.

D Frank Hsu1, Marcelo T LaFleur2, Ilyas Orazbek1

  • 1Laboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY 10023, USA.

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|February 15, 2022
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Summary
This summary is machine-generated.

Combinatorial fusion algorithm (CFA) improves document classification for UN Sustainable Development Goals (SDGs). Combining diverse AI models enhances precision, but only if individual models perform well.

Keywords:
LDAcognitive diversitycombinatorial fusion algorithm (CFA)rank combinationrank-score characteristic (RSC) functionscore combinationsemantic websustainable development goals (SDGs)topic model

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Information Science

Background:

  • Classifying documents for the 17 UN Sustainable Development Goals (SDGs) is challenging due to overlapping goals and contextual nuances.
  • Existing classification methods struggle with the complexity and diversity of SDG-related information.

Purpose of the Study:

  • To enhance the precision of document classification for UN SDGs.
  • To apply the Combinatorial Fusion Algorithm (CFA) framework for integrating multiple machine learning models.
  • To analyze the impact of model diversity and performance on classification accuracy.

Main Methods:

  • Utilized the Combinatorial Fusion Algorithm (CFA), a machine learning and artificial intelligence (ML/AI) framework.
  • Combined a topic model classifier (Model A) and a semantic link classifier (Model B).
  • Characterized individual models using the rank-score characteristic (RSC) function and cognitive diversity (CD).
  • Evaluated combined model performance via score and rank combinations against human expert classifications.

Main Results:

  • The integration of Model A and Model B using CFA improved classification precision for UN SDGs.
  • Classification precision enhancement was contingent upon the individual models exhibiting strong performance and significant diversity.
  • Analysis using RSC and CD provided insights into the contribution of each model to the combined outcome.

Conclusions:

  • The effectiveness of CFA in improving SDG classification precision relies on the quality and diversity of the constituent models.
  • CFA offers a viable framework for leveraging ML/AI to address complex classification tasks in areas like sustainable development.
  • Future work should focus on optimizing model selection and diversity metrics within CFA for maximum classification performance.