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Extension of labeled multiple attribute decision making based on fuzzy neighborhood three-way decision

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Abstract

Weight assignment of attribute is considered as a key part of multiple attribute decision making (MADM), and this is also applicable to labeled multiple attribute decision making (LMADM) that is a decision theory specially proposed for the dataset with labels. However, regarding the decision making of massive data characterized by redundancy and uncertainty, more means including attribute selection and uncertainty processing should be considered to solve these problems. Based on the traditional framework of LMADM, this paper deduces a new framework to adapt to the decision making of massive data. With respect to the uncertainty generated from data and decision process, a fuzzy neighborhood three-way decision model (FN3WD) is proposed, in which the fuzzy neighborhood relationship can address the uncertainty of data and the three-way decision theory can deal with the uncertainty of decision process. Finally, the experimental results illustrate the superiority of FN3WD and verify the effectiveness of the proposed framework of the extended LMADM by using some benchmarked datasets and the Commercial Modular Aero-Propulsion System Simulation dataset.

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Notes

  1. Usually, the decision attribute set contains only one attribute, and our work is based on the case of single decision attribute. In addition, the decision attribute is usually denoted by label in some cases.

  2. http://archive.ics.uci.edu/ml/datasets.html.

  3. https://sci2s.ugr.es/keel/datasets.php.

  4. http://weka.wikispaces.com, v3.6.13.

  5. http://archive.ics.uci.edu/ml/datasets/Iris.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 61903015, 61973011 and 61803013), the Fundamental Research Funds for the Central Universities (Grant Nos. 6142004180501 and ZG140S1993), the Capital Science & Technology Leading Talent Program (Grant No. Z191100006119029), National key Laboratory of Science and Technology on Reliability and Environmental Engineering (Grant No. WDZC2019601A304), as well as the China Postdoctoral Science Foundation (Grant No. 2019M650438).

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Authors and Affiliations

  1. School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China

    Mingliang Suo, Yujie Cheng, Chen Lu & Laifa Tao

  2. Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing, 100191, China

    Mingliang Suo, Yujie Cheng, Chen Lu & Laifa Tao

  3. Aviation University of Air Force, Jinzhou, 121018, China

    Chunqing Zhuang

  4. School of Aeronautic Science & Engineering, Beihang University, Beijing, China

    Yu Ding

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  1. Mingliang Suo
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Correspondence to Laifa Tao.

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Appendix

Appendix

In this part, the test results of the two parameters with respect to FN3WD by using the other datasets are shown in Figs. 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, and 30. Therein, the datasets, avila, clave and mushroom, are with the step of 0.1, which does not affect the conclusion of parameters selection.

Fig. 20
figure 20

Atoms: \(0 < \zeta \le 0.4\), \(0.75 \le \delta \le 1\) and \(0.75 \le \zeta \le 1\), \(0 \le \delta \le 0.75\)

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Fig. 21
figure 21

Australian: \(0 < \zeta \le 0.5\), \(0.55 \le \delta \le 1\)

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Fig. 22
figure 22

Avila: \(0 < \zeta \le 0.7\), \(0.8 \le \delta \le 1\)

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Fig. 23
figure 23

Clave: \(0 < \zeta \le 0.6\), \(0.8 \le \delta < 1\)

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Fig. 24
figure 24

Breast: \(0 < \zeta \le 0.55\), \(0.75 \le \delta \le 1\)

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Fig. 25
figure 25

Cleve: \(0 < \zeta \le 0.4\), \(0.8 \le \delta < 1\)

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Fig. 26
figure 26

Ecoli: \(0 < \zeta \le 0.65\), \(0.95 \le \delta < 1\)

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Fig. 27
figure 27

Vote: \(0 < \zeta \le 0.6\), \(0 \le \delta \le 1\)

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Fig. 28
figure 28

wdbc: \(0 < \zeta \le 0.65\), \(0.9 \le \delta < 1\)

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Fig. 29
figure 29

Wine: \(0 < \zeta \le 0.4\), \(0.85 \le \delta < 1\)

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Fig. 30
figure 30

Mushroom: \(0 < \zeta \le 0.5\), \(0 \le \delta < 1\)

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Suo, M., Cheng, Y., Zhuang, C. et al. Extension of labeled multiple attribute decision making based on fuzzy neighborhood three-way decision. Neural Comput & Applic 32, 17731–17758 (2020). https://doi.org/10.1007/s00521-020-04946-z

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