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). When measuring the relevance of a publication or document with respect to the 17 Sustainable Development Goals (SDGs) of the United Nations, a classification scheme is used. However, this classification process is a challenging task due to the overlapping goals and contextual differences of those diverse SDGs. In this paper, we use CFA to combine a topic model classifier (Model A) and a semantic link classifier (Model B) to improve the precision of the classification process. We characterize and analyze each of the individual models using the RSC function and CD between Models A and B. We evaluate the classification results from combining the models using a score combination and a rank combination, when compared to the results obtained from human experts. In summary, we demonstrate that the combination of Models A and B can improve classification precision only if these individual models perform well and are diverse.
Highlights
Powerful classification tools are used to help organize, search, and understand our increasingly digitized knowledge
model classifier (Model A) uses a Latent Dirichlet allocation (LDA) algorithm to develop a probabilistic model of the 17 Sustainable Development Goals (SDGs) that can be used for classification [21]
We examine the classification results of using both combined performance of the combined models with a small sample that allows us to peer into clasmodels score combination, andclassification
Summary
Powerful classification tools are used to help organize, search, and understand our increasingly digitized knowledge. Topic models have been used to categorize works in bioinformatics, for instance, among many other fields [1] These tasks are challenging when there is a lack of sufficient well-labeled training data and when documents belong to multiple categories in different proportions [2]. Combinatorial fusion algorithm (CFA) provides methods and algorithms for combining multiple scoring systems using the rank-score characteristic (RSC) function and cognitive diversity (CD) [3,4] It has been used widely in protein structure prediction [5], ChIP-seq peak detection [6], virtual screening and drug discovery [7,8], target tracking [9], stress detection [10,11], portfolio management [12], visual cognition [13], wireless network handoff detection [14], combining classifiers with diversity and accuracy [15], and text categorization [16], to name just a few (see [17,18,19] and the references within)
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