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Incremental concept-cognitive learning based on attribute topology
International Journal of Approximate Reasoning ( IF 3.9 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.ijar.2019.12.010
Tao Zhang , He-he Li , Meng-qi Liu , Mei Rong

Abstract Incremental learning is an alternative approach for maintaining knowledge by utilizing previous computational results of dynamic data contexts. As a new and important part of incremental learning, incremental Concept-cognitive learning (CCL) is an emerging field of concerning evolution of object or attributes sets and dynamic knowledge processing in the dynamic big data. However, existing incremental CCL algorithms still face some challenges that improve the generalization ability of new concepts, and the previously acquired knowledge should be efficiently utilized to reduce the computational complexity of the algorithm. At the same time, formal concept analysis has become a potential direction of cognitive computing, which can describe the processes of CCL. Attribute topology (AT) as a new representation of formal concepts can clearly display the relationship between new data and original data for reducing the complexity of the CCL process; therefore, we present an incremental concept-cognitive algorithm based on AT for incremental concept calculation, which is expressed by a concept tree. First, a relationship between the new object and some of the original objects is established. Then, on the basis of this finding, we propose an algorithm for updating the concept and presenting them through a concept tree. The algorithm determines the position and subtree of the new object by the relationship between the object and the original objects. Finally, an example is presented to demonstrate that the concept update algorithm based on AT is feasible and effective, and different orders of increments will result in different concept tree structures.

中文翻译:

基于属性拓扑的增量概念认知学习

摘要 增量学习是一种利用动态数据上下文的先前计算结果来维护知识的替代方法。作为增量学习的一个新的重要组成部分,增量概念认知学习(CCL)是一个新兴领域,涉及动态大数据中对象或属性集的演化和动态知识处理。然而,现有的增量 CCL 算法仍面临一些挑战,提高新概念的泛化能力,应有效利用先前获得的知识来降低算法的计算复杂度。同时,形式化概念分析成为认知计算的一个潜在方向,可以描述CCL的过程。属性拓扑(AT)作为形式概念的新表示,可以清晰地展示新数据与原始数据的关系,降低CCL过程的复杂度;因此,我们提出了一种基于AT的增量概念认知算法,用于增量概念计算,用概念树表示。首先,建立新对象和一些原始对象之间的关系。然后,在此发现的基础上,我们提出了一种更新概念并通过概念树呈现它们的算法。该算法通过对象与原始对象之间的关系来确定新对象的位置和子树。最后,通过一个例子证明了基于AT的概念更新算法的可行性和有效性,
更新日期:2020-03-01
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