Laboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY 10023, USA.
Department of Economic and Social Affairs, United Nations, New York, NY 10017, USA.
Sensors (Basel). 2022 Jan 29;22(3):1067. doi: 10.3390/s22031067.
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.
组合融合算法（CFA）是一种机器学习和人工智能（ML/AI）框架，用于使用排名评分特征（RSC）函数和认知多样性（CD）组合多个评分系统。在衡量出版物或文档与联合国 17 个可持续发展目标（SDGs）的相关性时，使用分类方案。然而，由于这些不同 SDG 的目标重叠和背景差异，这一分类过程具有挑战性。在本文中，我们使用 CFA 将主题模型分类器（模型 A）和语义链接分类器（模型 B）结合起来，以提高分类过程的精度。我们使用 RSC 函数和模型 A 和 B 之间的 CD 来描述和分析每个单独的模型。我们通过评分组合和模型组合来评估模型组合的分类结果，与人类专家获得的结果进行比较。总之，我们证明了只有当这些单个模型表现良好且具有多样性时，模型 A 和 B 的组合才能提高分类精度。
