Modelling Enterprise’s Coordinated Development Strategy with a Soft Fuzzy Rough Set

• Published in: Discrete dynamics in nature and society Vol. 2021; pp. 1 - 7
• Main Authors: Wang, Tongtong, Cui, Jianing
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Approximation; Data mining; Datasets; Decision theory; Ecological effects; Economic development; Environmental impact; Fuzzy set theory; Fuzzy sets; Machine learning; Mathematical models; Model accuracy; Noise; Parameters; Performance evaluation; Performance prediction; Prediction models; Rough set models; Set theory; Social responsibility; Support vector machines; Sustainable development; Training
• ISSN: 1026-0226; 1607-887X
• DOI: 10.1155/2021/3250306

With the concept of sustainable development, enterprises are facing severe challenges in ecological protection and economic development. Approaches to improving effectiveness of the coordinated development strategy must continue to evolve to address uncertainty and hazards that may be encountered in the future. We propose a coordinated development strategy model based on the combination of soft fuzzy and rough set theory and construct its prediction model. For the multistrategy dataset in the paper, parameter for each kind shall be selected through converting the multistrategy data into two prediction datasets. An algorithm transformed by SFRC shall be subject to weighted average for each parameter. Furthermore, we use training methods and soft fuzzy rough sets’ learning algorithm to calculate, and the evaluating indicator rough set is constructed with a three-tier model structure. After the final rough set training is completed, test results show that the rough set model which has a higher rating accuracy builds a better completed business performance evaluation. By comparing the prediction effect, both SVM algorithm and multistrategy prediction model for the soft fuzzy rough set in the paper can realize effective prediction for the enterprise’s coordinated development strategy. Moreover, the prediction result obtained at the time of adopting boundary to get the expected value is superior to that of giving one fixed threshold. It shows that the prediction performance of the algorithm in the paper is more excellent and represents the advantage of the algorithm prediction performance at the time of adopting boundary to get the expected value. The model provides support methods to assist enterprise management in making more efficient and scientific decisions for enterprise’s coordinated development.