Formal concept analysis has seldom been employed for classification. This is mainly due to (1) the high time and space complexity of concept lattice construction, and (2) the difficulty of concept lattice based prediction. Inspired by information fusion, this paper introduces a new algorithm named CECS, which constructs a concept set instead of a lattice to ensure efficiency and enable direct classification. Regarding concept set construction, we define sub-formal context by grouping objects with the same label within the labeled formal context. Subsequently, we induce the label concept from the sub-formal context based on each individual entity (object or attribute). In this way, the information intrinsic to each label can be clearly expressed. At the same time, it reduces time consumption. Regarding classification, we define label confidence through fusing object and attribute induced concept sets. Respective calculation does not require additional prediction methods, thus becoming more efficient. Experiments are conducted on fifteen public datasets from UCI and KEEL in comparison with eight classical classification algorithms. Results validate the time complexity analysis, and show competitive classification performance of our algorithm. The source code is available at .

中文翻译：

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用于分类的个体实体诱导标签概念集：信息融合观点


形式概念分析很少用于分类。这主要是由于（1）概念格构建的时间和空间复杂度高，以及（2）基于概念格的预测困难。受信息融合的启发，本文引入了一种名为CECS的新算法，该算法构建概念集而不是格，以保证效率并实现直接分类。关于概念集构建，我们通过在标记的形式上下文中对具有相同标签的对象进行分组来定义子形式上下文。随后，我们根据每个单独的实体（对象或属性）从亚形式上下文中归纳出标签概念。这样，每个标签固有的信息就可以清晰地表达出来。同时，它减少了时间消耗。关于分类，我们通过融合对象和属性诱导的概念集来定义标签置信度。各自的计算不需要额外的预测方法，从而变得更加高效。在 UCI 和 KEEL 的 15 个公共数据集上进行了实验，并与八种经典分类算法进行了比较。结果验证了时间复杂度分析，并显示了我们算法的有竞争力的分类性能。源代码可在 处获得。