Supervised machine learning (ML) algorithms are aimed at maximizing classification performance under available energy and storage constraints. They try to map the training data to the corresponding labels while ensuring generalizability to unseen data. However, they do not integrate meaning-based relationships among labels in the decision process. On the other hand, natural language processing (NLP) algorithms emphasize the importance of semantic information. In this paper, we synthesize the complementary advantages of supervised ML and NLP algorithms into one method that we refer to as SECRET (Semantically Enhanced Classification of REal-world Tasks). SECRET performs classifications by fusing the semantic information of the labels with the available data: it combines the feature space of the supervised algorithms with the semantic space of the NLP algorithms and predicts labels based on this joint space. Experimental results indicate that, compared to traditional supervised learning, SECRET achieves up to 14.0% accuracy and 13.1% F1 score improvements. Moreover, compared to ensemble methods, SECRET achieves up to 12.7% accuracy and 13.3% F1 score improvements. This points to a new research direction for supervised classification based on incorporation of semantic information.

中文翻译：

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秘密：现实世界任务的语义增强分类

监督机器学习 (ML) 算法旨在在可用能量和存储约束下最大化分类性能。他们尝试将训练数据映射到相应的标签，同时确保对看不见的数据的普遍性。然而，它们并没有在决策过程中整合标签之间基于意义的关系。另一方面，自然语言处理（NLP）算法强调语义信息的重要性。在本文中，我们将监督 ML 和 NLP 算法的互补优势综合到一种我们称为 SECRET（现实世界任务语义增强分类）的方法中。SECRET 通过将标签的语义信息与可用数据融合来执行分类：它结合了监督算法的特征空间和 NLP 算法的语义空间，并基于这个联合空间预测标签。实验结果表明，与传统的监督学习相比，SECRET 实现了高达 14.0% 的准确率和 13.1% 的 F1 分数提升。此外，与集成方法相比，SECRET 实现了高达 12.7% 的准确度和 13.3% 的 F1 分数改进。这为基于语义信息结合的监督分类指明了一个新的研究方向。