通过形式概念对布尔数据进行因式分解的语义探索

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2024-07-08 DOI:10.1016/j.ijar.2024.109247

Radim Belohlavek, Martin Trnecka

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引用次数: 0

摘要

我们利用现有的心理学数据，涉及人类概念、这些概念所涵盖的对象以及描述这些对象的二进制属性，来探索布尔矩阵因式分解的选定语义方面。我们的基本观点源于这样一个直观要求，即从数据中计算出的因子应代表数据中潜在的自然类别。我们针对利用形式概念构建因数的因数分解算法研究了这一想法。我们提供了一些实验观察结果，这些观察结果表明，所检验的因式分解方法能提供语义上合理的因式，这些因式与所检验领域的重要人类概念相似。

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Semantic explorations in factorizing Boolean data via formal concepts

We use now available psychological data involving human concepts, objects covered by these concepts, and binary attributes describing the objects to explore selected semantic aspects of Boolean matrix factorization. Our basic perspective derives from the intuitive requirement that the factors computed from data should represent natural categories latently present in the data. This idea is examined for factorization algorithms that utilize formal concepts to build factors. We provide several experimental observations which imply that the inspected factorization methods deliver semantically sound factors that resemble significant human concepts of the examined domains.

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来源期刊

International Journal of Approximate Reasoning 工程技术-计算机：人工智能

CiteScore

6.90

自引率

12.80%

发文量

170

审稿时长

67 days

期刊介绍： The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.