For the multi-decision attribute case adaptation problem,a multi-case derivational adaptation method based on decision attribute correlation is proposed.First,the application of the classifier chain approach in the case-induced adaptation problem is investigated,followed by an explanation of the essential concepts of decision attribute-dependent multi-case induced adapter chains.Second,Hellinger distance is utilized for the development of unique views on fitness cases that can be incorporated into the decision-making process.Finally,the weighted naive Bayesian algorithm is utilized as the base adapter technique to construct the adapter chain,resolve the probability distribution of the decision attribute values,and settle on the decision attribute values.Experiments show that the differentiated case view in the adaptation process makes it possible for the adapter chain to successfully link decision attributes and change how they depend on conditional attributes.The proposed method efficiently addresses multi-case derivational adaptation problem with correlated decision attributes.
decision attributecase adaptationmulti-case derivationaldifferentiated view construction
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