JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (9): 1-8, 17.doi: 10.6040/j.issn.1671-9352.0.2022.561

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Multi-case derivational adaptation with correlated decision attributes

Jianhua ZHANG1(),Dandan WEN1,2,*(),Longfei HE1   

  1. 1. School of Management, Zhengzhou University, Zhengzhou 450001, Henan, China
    2. School of Logistics and E-commerce, Henan University of Animal Husbandry and Economy, Zhengzhou 450044, Henan, China
  • Received:2022-10-27 Online:2024-09-20 Published:2024-10-10
  • Contact: Dandan WEN E-mail:tjzhangjianhua@163.com;mydwdd@126.com

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Abstract:

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.

Key words: decision attribute, case adaptation, multi-case derivational, differentiated view construction

CLC Number: 

  • F270

Table 1

Symbolic representation of the multi-decision attribute case adaptation problem"

案例条件属性C决策属性D
C1 C2 … Ch D1 D2 … Dk
U1 C11 C12 … C1h D11 D12 … D1k
U2 C21 C22 … C2h D21 D22 … D2k
… … … … … … … … …
Ue Ce1 Ce2 … Ceh De1 De2 … Dek
U0 C01 C02 … C0h

Fig.1

Flowchart of adapted solution trajectory solver"

Table 2

Problems to be solved"

编号 D3 D2 D1 C1 C2 C3 C4 C5 C6 C7 C8 C9
U01 3 3 3 4 3 5 2 2 2 1 2 3
U02 3 3 4 3 4 2 1 3 2 1 2 5
U03 4 4 4 4 3 5 1 2 2 1 2 3
U04 3 3 4 2 3 4 1 2 1 1 2 5

Table 3

View of differentiation cases based on Hellinger distance"

权重 ω11 ω21 ω31 ω41 ω51 ω61 ω71 ω81 ω91
数值 0.15 0.15 0.12 0.10 0.13 0.08 0.06 0.05 0.16
权重 ω12 ω22 ω32 ω42 ω52 ω62 ω72 ω82 ω92 ωD12
数值 0.13 0.13 0.10 0.08 0.11 0.07 0.05 0.04 0.13 0.16
权重 ω13 ω23 ω33 ω43 ω53 ω63 ω73 ω83 ω93 ωD13 ωD23
数值 0.11 0.11 0.09 0.07 0.09 0.06 0.04 0.04 0.11 0.14 0.14

Table 4

Probability distribution of decision attributes of the problem to be solved"

dD1 D2D3
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
U01 0.04 0.13 0.30 0.15 0.01 0.01 0.14 0.40 0.08 0.01 0.07 0.15 0.30 0.11 0.02
U02 0.03 0.06 0.29 0.13 0.05 0.01 0.07 0.37 0.12 0.01 0.01 0.10 0.44 0.03 0.02
U03 0.02 0.17 0.13 0.25 0.04 0.01 0.01 0.28 0.34 0.04 0.01 0.01 0.19 0.44 0.04
U04 0.04 0.08 0.19 0.21 0.04 0.01 0.03 0.24 0.22 0.01 0.01 0.03 0.29 0.16 0.01

Fig.2

Comparison of the adaptation accuracy of the six algorithms"

Fig.3

Comparison of the adaptation precision of the six algorithms"

Fig.4

Comparison of fitness recall of six algorithms"

Fig.5

Comparison of adapted F1 values for six algorithms"

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