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Uncertainty measurement for a three heterogeneous information system and its application in feature selection

  • Mathematical methods in data science
  • Published:
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Abstract

An information system (IS) is an important model in rough set theory. In practice applications, we often encounter an IS with different structures of information values. A three heterogeneous information system (3HIS) means an IS whose information values contain three types of data (i.e., scaled types, ordered types and normal types). This paper studies uncertainty measurement for a 3HIS and its application in feature selection. A 3HIS is first put forward. Then, the fuzzy relation on the object set with respect to each subsystem is defined. Next, four measure tools are used to assess the uncertainty of a 3HIS according to the fuzzy information granules induced by the defined fuzzy relation. Combining with the proposed measures, an application for feature selection in a 3HIS is given, and the corresponding algorithms based on the uncertainty measures are presented. Finally, numerical experiments are carried out and effectiveness analysis from the statistics perspective is done as so to evaluate the performance of the presented algorithms. The experimental results indicate that the proposed algorithm is more effective than some existing algorithms. These results will contribute to understanding the essence of uncertainty in a 3HIS.

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References

  • Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R, Ghandeharioun A, Bahadorian B, Sani ZA (2013) A data mining approach for diagnosis of coronary artery disease. Comput Methods Programs Biomed 111(1):52–61

    Article  Google Scholar 

  • Alizadehsani R, Zangooei MH, Hosseini MJ, Habibi J, Khosravi A, Roshanzamir M, Khozeimeh F, Sarrafzadegan N, Nahavandi S (2016) Coronary artery disease detection using computational intelligence methods. Knowl-Based Syst 109:187–197

    Article  Google Scholar 

  • Ananthanarayana VS, Narasimha Murty M, Subramanian DK (2003) Tree structure for efficient data mining using rough sets. Patt Recgn Lett 24:851–862

    Article  Google Scholar 

  • Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA (2017) Computer aided decision making for heart disease detection using hybrid neural network-genetic algorithm. Comput Methods Programs Biomed 141:19–26

    Article  Google Scholar 

  • Chan CC (1998) A rough set approach to attribute generalization in data mining. Inf Sci 107:169–176

    Article  MathSciNet  Google Scholar 

  • Corsato C, Pelessoni R, Vicig P (2019) Nearly-linear uncertainty measures. Int J Approx Reason 114:1–28

    Article  MathSciNet  Google Scholar 

  • Dai JH, Wang WT, Mi JS (2013) Uncertainty measurement for interval-valued information systems. Inf Sci 251:63–78

    Article  MathSciNet  Google Scholar 

  • Düntsch I, Gediga G (1998) Uncertainty measures of rough set prediction. Artif Intell 106:109–137

    Article  MathSciNet  Google Scholar 

  • Ferrag MA, Maglaras L, Moschoyiannis S, Janicke H (2020) Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J Inf Secur Appl 50:102419

    Google Scholar 

  • Hu M, Tsang ECC, Guo YT, Xu WH (2021) Fast and robust attribute reduction based on the separability in fuzzy decision systems. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3040803

    Article  Google Scholar 

  • Leung Y, Fischer MM, Wu WZ, Mi JS (2008) A rough set approach for the discovery of classification rules in interval-valued information systems. Int J Approx Reason 47:233–246

    Article  MathSciNet  Google Scholar 

  • Li ZW, Zhang PF, Ge X, Xie NX, Zhang GQ, Wen CF (2019) Uncertainty measurement for a fuzzy relation information system. IEEE Trans Fuzzy Syst 27:2338–2352

    Google Scholar 

  • Liang JY, Qian YH (2008) Information granules and entropy theory in information systems. Sci China Ser F 51:1427–1444

    MathSciNet  MATH  Google Scholar 

  • Mac ParthaláIn N, Jensen R (2013) Unsupervised fuzzy-rough set-based dimensionality reduction. Inf Sci 229:106–121

    Article  MathSciNet  Google Scholar 

  • Meenachi L, Ramakrishnan S (2020) Differential evolution and ACO based global optimal feature selection with fuzzy rough set for cancer data classification. Soft Comput 24:18463–18475

    Article  Google Scholar 

  • Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356

    Article  Google Scholar 

  • Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht

    Book  Google Scholar 

  • Qian WB, Shu WH (2018) Attribute reduction in incomplete ordered information systems with fuzzy decision. Appl Soft Comput 73:242–253

    Article  Google Scholar 

  • Qian YH, Liang JY, Wu WZ, Dang CY (2011) Information granularity in fuzzy binary GrC model. IEEE Trans Fuzzy Syst 19(2):253–264

    Article  Google Scholar 

  • Singh S, Shivam S, Tanmoy S, Gaurav S (2020) A fuzzy similarity-based rough set approach for attribute selection in set-valued information systems. Soft Comput 24:4675–4691

    Article  Google Scholar 

  • Sun L, Xu JC, Tian Y (2012) Feature selection using rough entropy-based uncertainty measures in incomplete decision systems. Knowl Based Syst 36:206–216

    Article  Google Scholar 

  • Sun BZ, Ma WM, Chen DG (2014) Rough approximation of a fuzzy concept on a hybrid attribute information system and its uncertainty measure. Inf Sci 284:60–80

    Article  MathSciNet  Google Scholar 

  • Sun L, Yin TY, Ding WP, Qian YH, Xu JC (2020) Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems. Inf Sci 537:401–424

    Article  MathSciNet  Google Scholar 

  • Sun L, Wang LY, Ding WP, Qian YH, Xu JC (2021) Feature selection using fuzzy neighborhood entropy-based uncertainty measures for fuzzy neighborhood multigranulation rough sets. IEEE Trans Fuzzy Syst 29(1):19–33

    Article  Google Scholar 

  • Swiniarski RW, Skowron A (2003) Rough set methods in featureselection and recognition. Pattern Recogn Lett 24:833–849

    Article  Google Scholar 

  • Tan AH, Shi SW, Wu WZ, Li JJ, Pedrycz W (2020) Granularity and entropy of intuitionistic fuzzy information and their applications. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.2973379

    Article  Google Scholar 

  • Tsumoto S (1998) Automated extraction of medical expert system rules from clinical databases based on rough set theory. Inf Sci 112:67–84

    Article  Google Scholar 

  • Velayutham C, Thangavel K (2011) Unsupervised quick reduct algorithm using rough set theory. J Comput Sci Technol 9(3):193–201

    Google Scholar 

  • Velayutham C, Thangavel K (2012) A novel entropy based unsupervised feature selection algorithm using rough set theory. In IEEE-International Conference On Advances In Engineering, Science And Management, pp 156–161, 2012

  • Wang CZ, Huang Y, Shao MW, Chen DG (2019) Uncertainty measures for general fuzzy relations. Fuzzy Sets Syst 360:82–96

    Article  MathSciNet  Google Scholar 

  • Wang CZ, Huang Y, Shao MW, Hu QH, Chen DG (2020) Feature selection based on neighborhood self-information. IEEE Trans Cybern 50(9):4031–4042

    Article  Google Scholar 

  • Xie NX, Liu M, Li ZW, Zhang GQ (2019) New measures of uncertainty for an interval-valued information system. Inf Sci 470:156–174

    Article  MathSciNet  Google Scholar 

  • Yu B, Guo LK, Li QG (2019) A characterization of novel rough fuzzy sets of information systems and their application in decision making. Expert Syst Appl 122:253–261

    Article  Google Scholar 

  • Yu JH, Li YQ, Chen MH, Zhang B, Xu WH (2019) Decision-theoretic rough set in lattice-valued decision information system. J Intell Fuzzy Syst 36(4):3289–3301

    Article  Google Scholar 

  • Zhang XX, Chen DG, Tsangc EC (2017) Generalized dominance rough set models for the dominance intuitionistic fuzzy information systems. Inf Sci 378:1–25

    Article  MathSciNet  Google Scholar 

  • Zhang GQ, Li ZW, Wu WZ, Liu XF, Xie NX (2018) Information structures and uncertainty measures in a fully fuzzy information system. Int J Approx Reason 101:119–149

    Article  MathSciNet  Google Scholar 

  • Zhang GQ, Li ZW, Liu M, Xie NX (2019) \(cc\)-reduction in a fully fuzzy information system. J Intell Fuzzy Syst 36(6):6589–6604

Download references

Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions, which have helped immensely in improving the quality of the paper. This work is supported by National Natural Science Foundation of China (11971420), Key Laboratory of Software Engineering in Guangxi University for Nationalities (2021-18XJSY-03), Guangxi Science and Technology Program (2017AD23056), Natural Science Foundation of Guangxi (AD19245102, 2020GXNSFAA159155, 2018GXNSFDA294003) and Special Scientific Research Project of Young Innovative Talents in Guangxi (2019AC20052).

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Authors and Affiliations

  1. School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, Guangxi, People’s Republic of China

    Gangqiang Zhang & Liangdong Qu

  2. School of Mathematics and Statistics, Yulin Normal University, Yulin, 537000, Guangxi, People’s Republic of China

    Yan Song

  3. School of Mathematics and Physics, Guangxi University for Nationalities, Nanning, 530006, Guangxi, People’s Republic of China

    Shimin Liao

  4. Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, 537000, Guangxi, People’s Republic of China

    Zhaowen Li

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  1. Gangqiang Zhang
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  2. Yan Song
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  3. Shimin Liao
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  4. Liangdong Qu
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Contributions

GQZ designs the overall structure of the paper and writes the paper; Y. Song designs the overall structure of the paper; SML collects the data; LDQ implements the proposed method; ZWL writes the paper and improves the language.

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Correspondence to Yan Song or Zhaowen Li.

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Zhang, G., Song, Y., Liao, S. et al. Uncertainty measurement for a three heterogeneous information system and its application in feature selection. Soft Comput 26, 1711–1725 (2022). https://doi.org/10.1007/s00500-021-06722-0

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