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An improved lasso regression model for evaluating the efficiency of intervention actions in a system reliability analysis

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Abstract

A conventional LASSO (least absolute shrinkage and selection operator) regression model utilizing the Pythagorean fuzzy sets in a system reliability analysis is developed. Overall, the Pythagorean fuzzy multivariate regression analysis enables decision makers to correctly identify the relationships between a set of responses in the form of fuzzy or non-fuzzy interpretive variables. The interpretability of the model is significantly improved by the proposed Pythagorean fuzzy LASSO regression model (PFLRM). Thus, a system reliability analysis is considered as an application of the study to evaluate the efficiency and effectiveness of the proposed PFLRM. There is no doubt that a system reliability analysis is vital to improve the safety performance of chemical processing industries, where an extensive number of industrial accidents occur annually. These accidents have subsequently highlighted the failure of some of the intervention actions to keep the systems safely in operation. The results illustrate a better performance with higher accuracy with the proposed PFLRM compared with the existing number of fuzzy regression models, particularly in the availability of non-informative variables.

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Abbreviations

CV:

Validation criterion

FLRM:

Fuzzy linear regression method

HFACS:

Human factor analysis and classification system

IFN:

Intuitionistic fuzzy numbers

IFS:

Intuitionistic fuzzy set

LASSO:

Least absolute shrinkage and selection operator

MARE:

Absolute relative error

OI:

Organizational influences

PFLRM:

Pythagorean fuzzy LASSO regression model

PFN:

Pythagorean fuzzy numbers

PFS:

Pythagorean fuzzy set

PUA:

Preconditions for unsafe acts

RMSE:

Root mean square error

SF:

Supervisory factors

SM:

Similarity measure

UA:

Unsafe acts

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Authors and Affiliations

  1. University of Lisbon, Lisboa, Portugal

    Mohammad Yazdi

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China , Chengdu, China

    Noorbakhsh Amiri Golilarz

  3. Industrial Engineering Department, Quchan University of Technology, Quchan, Iran

    Arman Nedjati

  4. Industrial Engineering Department, Near East University, Nicosia/KKTC, North Cyprus, Turkey

    Kehinde A. Adesina

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  1. Mohammad Yazdi
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  4. Kehinde A. Adesina
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Correspondence to Mohammad Yazdi.

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Yazdi, M., Golilarz, N.A., Nedjati, A. et al. An improved lasso regression model for evaluating the efficiency of intervention actions in a system reliability analysis. Neural Comput & Applic 33, 7913–7928 (2021). https://doi.org/10.1007/s00521-020-05537-8

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