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RETRACTED ARTICLE: The utilization of rough set theory and data reduction based on artificial intelligence in recommendation system

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This article was retracted on 20 December 2022

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Abstract

To improve the effectiveness of allocating the education environmental resources in college music, an allocation model for education environmental resource based on rough set and data reduction is proposed. First, an allocation model of risk preference for education environmental resource based on rough set and data reduction classifier is developed. This model divides risk preference into the existing risk preference and non-existent risk preference. Also, it trains and optimizes the data set for the allocation of education environmental resources by rough set and data reduction classifier. Second, as the rough set and data reduction classifier may fail sometimes in predicting the allocation of education environmental resource, the fuzzy clustering based on rough set is optimized, and combined with the data reduction theory to enhance the effectiveness of allocating the education environmental resources. Finally, based on the simulation research on the allocation model of education environmental resources, the proposed algorithm can obtain a more reasonable allocation of education environmental resources, which embodies the effectiveness of the algorithm. The data reduction classifier of a rough set is used to train and optimize the data set of the allocation of education environmental resources, and visual c++ is used to realize the allocation of education environmental resources. The simulation results show that the algorithm can effectively allocate education environmental resources. The model takes into account the characteristics of the incompatibility between the evaluation of historical data and the complex index system in the evaluation decision-making. It can divide the evaluation decision-making table reasonably, and realize the objective calculation of the weights of each evaluation index based on the rough set condition through hierarchical calculation.

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References

  • Ahmed NSS, Acharjya DP (2019) A framework for various attack identification in MANET using multi-granular rough set. Int J Inf Secur Priv 13(4):28–52

    Article  Google Scholar 

  • Amin F, Fahmi A (2019) Human Immunodeficiency Virus (HIV) infection model based on triangular neutrosophic cubic hesitant fuzzy number. Int J Biomath 12(05):1950055

    Article  Google Scholar 

  • Amin F, Fahmi A, Abdullah S et al (2018) Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. J Intell Fuzzy Syst 34(4):2401–2416

    Article  Google Scholar 

  • Amin F, Fahmi A, Abdullah S (2019) Dealer using a new trapezoidal cubic hesitant fuzzy TOPSIS method and application to group decision-making program. Soft Comput 23(14):5353–5366

    Article  MATH  Google Scholar 

  • Chen M, Shen C (2019) The correlation analysis between the service quality of intelligent library and the behavioral intention of users. Electron Libr 38(1):95–112. https://doi.org/10.1108/EL-07-2019-0163

    Article  MathSciNet  Google Scholar 

  • Egan MJ, Angel SM, Sharma SK (2018) Optimizing data reduction procedures in spatial heterodyne Raman spectroscopy with applications to planetary surface analogs. Appl Spectrosc 72(6):933–942

    Article  Google Scholar 

  • Fahmi A, Abdullah S, Amin F et al (2017) Aggregation operators on triangular cubic fuzzy numbers and its application to multi-criteria decision making problems. J Intell Fuzzy Syst 33(6):3323–3337

    Article  Google Scholar 

  • Fahmi A, Abdullah S, Amin F et al (2018a) Weighted average rating (war) method for solving group decision making problem using triangular cubic fuzzy hybrid aggregation (tcfha). Punjab Univ J Math 50(1):23–34

    MathSciNet  Google Scholar 

  • Fahmi A, Abdullah S, Amin F et al (2018b) Some geometric operators with triangular cubic linguistic hesitant fuzzy number and their application in group decision-making. J Intell Fuzzy Syst 35(2):2485–2499

    Article  Google Scholar 

  • Fahmi A, Amin F, Abdullah S et al (2018c) Cubic fuzzy Einstein aggregation operators and its application to decision-making. Int J Syst Sci 49(11):2385–2397

    Article  MathSciNet  MATH  Google Scholar 

  • Fahmi A, Amin F, Smarandache F et al (2018d) Triangular cubic hesitant fuzzy Einstein hybrid weighted averaging operator and its application to decision making. Symmetry 10(11):658

    Article  MATH  Google Scholar 

  • Fahmi A, Abdullah S, Amin F et al (2019a) Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making. Soft Comput 23(14):5753–5783

    Article  MATH  Google Scholar 

  • Fahmi A, Abdullah S, Amin F (2019b) Cubic uncertain linguistic powered Einstein aggregation operators and their application to multi-attribute group decision making. Math Sci 13(2):129–152

    Article  MathSciNet  MATH  Google Scholar 

  • Fahmi A, Abdullah S, Amin F et al (2019c) Trapezoidal linguistic cubic fuzzy TOPSIS method and application in a group decision making program. J Intell Syst 29(1):1283–1300

    Article  MATH  Google Scholar 

  • Fahmi A, Amin F, Khan M et al (2019d) Group decision making based on triangular neutrosophic cubic fuzzy einstein hybrid weighted averaging operators. Symmetry 11(2):180

    Article  MATH  Google Scholar 

  • Fahmi A, Abdullah S, Amin F et al (2019e) Trapezoidal cubic hesitant fuzzy aggregation operators and their application in group decision-making. J Intell Fuzzy Syst 36(4):3619–3635

    Article  Google Scholar 

  • Feng B, Chen M (2020) The impact of entrepreneurial passion on psychology and behavior of entrepreneurs. Front Psychol 11:1733. https://doi.org/10.3389/fpsyg.2020.01733

    Article  Google Scholar 

  • Greco S, Matarazzo B, Slowinski R (1999) Handling missing values in rough set analysis of multi-attribute and multi-criteria decision problems. In: New directions in rough sets, data mining, and granular-soft computing, 7th international workshop, RSFDGrC ‘99, Yamaguchi, Japan, 9–11 Nov 1999, Proceedings. Springer

  • Greco S, Słowiński R, Zielniewicz P (2013) Putting dominance-based rough set approach and robust ordinal regression together. Decis Support Syst 54(2):891–903

    Article  Google Scholar 

  • Guo S, Wei M, Wang S et al (2019) Identify high-impact bug reports by combining the data reduction and imbalanced learning strategies. Appl Sci 9(18):3663

    Article  Google Scholar 

  • Herrera-Semenets V, Pérez-García OA, Hernández-León R et al (2018) A data reduction strategy and its application on scan and backscatter detection using rule-based classifiers. Expert Syst Appl 95:272–279

    Article  Google Scholar 

  • Li G (2019) An integrated model of rough set and radial basis function neural network for early warning of enterprise human resource crisis. Int J Fuzzy Syst 21(8):2462–2471

    Article  Google Scholar 

  • Li Y, Sun M, Yuan G et al (2019) Study on development sustainability of atmospheric environment in Northeast China by rough set and entropy weight method. Sustainability 11(14):3793

    Article  Google Scholar 

  • Lin Y, Xiao R, Yin LZ et al (2019) Developing spectrum-toxicity relationship with rough set theory for hepatotoxicity material basis of Polygonum multiflorum. China J Chin Mater Med 44(3):509–517

    Google Scholar 

  • Liu Y, Chen M (2018) From the aspect of STEM to discuss the effect of ecological art education on knowledge integration and problem-solving capability. EKOLOJI 27(106):1705–1711

    Google Scholar 

  • Liu Z, Wang C (2019) Design of traffic emergency response system based on internet of things and data mining in emergencies. IEEE Access 7:113950–113962. https://doi.org/10.1109/ACCESS.2019.2934979

    Article  Google Scholar 

  • Mazzorana B, Trenkwalder-Platzer H, Heiser M et al (2018) Quantifying the damage susceptibility to extreme events of mountain stream check dams using rough set analysis. J Flood Risk Manag 11(4):e12333

    Article  Google Scholar 

  • Mu S, Xiong Z (2019) Internet financial interest rate risk measure based on genetic rough set reduction. SOCA 13(4):309–321

    Article  Google Scholar 

  • Okurberberoglu E (2017) Outdoor experiential environmental education: an adult-centred intervention for the affective domain. Int Electron J Environ Educ 7:34–58

    Google Scholar 

  • Orlowska E (1984) A logic of indiscernibility relations. In: Symposium on computation theory. Springer, Berlin, pp 177–186

  • Pawlak Z (2003) Probability, truth and flow graph. Electron Notes Theor Comput Sci 82(4):1–9

    Article  MATH  Google Scholar 

  • Rathi R, Acharjya DP (2018) A rule based classification for vegetable production using rough set and genetic algorithm. Int J Fuzzy Syst Appl 7(1):74–100

    Google Scholar 

  • Shen C-W, Min C, Wang C-C (2019) Analyzing the trend of O2O commerce by bilingual text mining on social media. Comput Hum Behav 101:474–483. https://doi.org/10.1016/j.chb.2018.09.031

    Article  Google Scholar 

  • Suganya R, Jayashree LS (2019) Fuzzy rough set inspired rate adaptation and resource allocation using Hidden Markov Model (FRSIRA-HMM) in mobile ad hoc networks. Clust Comput 22(4):9875–9888

    Article  Google Scholar 

  • Upadhyay A, Anthal J, Shukla S (2019) Enhanced classification of LISS-III satellite image using rough set theory and ANN. Int J Cloud Comput 8(3):249–257

    Article  Google Scholar 

  • Yahia ME, Mahmod R, Sulaiman N et al (2000) Rough neural expert systems. Expert Syst Appl 18(2):87–99

    Article  Google Scholar 

  • Zhang W, Jia Y, Ge J et al (2019) Multi-index data dimension reduction approach and its applicability in the calculation of indicators of hydrological alteration. Hydrol Res 50(1):231–243

    Article  Google Scholar 

  • Zheng Y, Liu S (2020) Bibliometric analysis for talent identification by the subject–author–citation three-dimensional evaluation model in the discipline of physical education. Libr Hi Tech. https://doi.org/10.1108/LHT-12-2019-0248

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions. This work was supported by Hunan Provincial Social Science Results Review Committee Project “Study on the Industrial Integration of ‘Xiang’ Brand Traditional Music” (No. XSP19YBZ175).

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Authors and Affiliations

  1. School of Arts, Hunan City University, Yiyang, 413000, China

    Huizhi Cao

  2. Department of Culture and Arts Contents, Dongbang Culture University, Seoul, 100-744, South Korea

    Huizhi Cao

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  1. Huizhi Cao
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Correspondence to Huizhi Cao.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00500-022-07759-5"

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Cao, H. RETRACTED ARTICLE: The utilization of rough set theory and data reduction based on artificial intelligence in recommendation system. Soft Comput 25, 2153–2164 (2021). https://doi.org/10.1007/s00500-020-05286-9

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