Abstract
Combinatorial fusion algorithm (CFA) is a machine learning and artificial intelligence (ML/AI) framework for combining multiple scoring systems using the rank-score characteristic (RSC) function and cognitive diversity (CD). In this paper, we use CFA 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 document) are to each of the 17 Sustainable Development Goals (SDGs) of the United Nations. We characterize and analyze each of the individual models using the RSC function and the cognitive diversity between models A and B. We also evaluate the classification results of models A and B and combined models from score and rank combinations. Classification precision of the combined model is calculated is shown to uniformly improve over individual models when compared to classification results obtained by human experts.
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