Yanhui Zhai1,2,*, Rujie Chen1, Deyu Li1,2
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1833-1851, 2023, DOI:10.32604/iasc.2023.039553
Abstract Decision implication is a form of decision knowledge representation, which is able to avoid generating attribute implications that occur between condition attributes and between decision attributes. Compared with other forms of decision knowledge representation, decision implication has a stronger knowledge representation capability. Attribute granularization may facilitate the knowledge extraction of different attribute granularity layers and thus is of application significance. Decision implication canonical basis (DICB) is the most compact set of decision implications, which can efficiently represent all knowledge in the decision context. In order to mine all decision information on decision context under attribute granulating, this paper proposes an… More >
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