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An incremental approach to hierarchical feature selection by applying fuzzy rough set technique

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Abstract

In the age of big data, the number of class labels is increasing rapidly and there exists a hierarchical structure between different class labels. In the present paper, we revisit the existing granular computing approach to hierarchical classification. By revealing some limitations of approximation capacity, we develop a novel model for hierarchical classification. Then, we present a formal approach to feature selection for hierarchical decision tables by using fuzzy rough set theory. Correspondingly, an algorithm using relative discernibility relation is designed to select relevant feature subsets. Considering the fact that real data may vary dynamically with time, we also propose an incremental approach to hierarchical feature selection by using fuzzy rough set technique. An incremental algorithm for hierarchical feature selection is provided based on the sibling strategy. The experimental results demonstrate that the proposed approach is feasible and valid.

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References

  • Aho AV, Hopcroft JE, Ullman JD (1976) On finding lowest common ancestors in trees. Assoc Comput Mach 5(1):115–132

    MathSciNet  MATH  Google Scholar 

  • Blake CL, Merz CJ (1998) UCI repository of machine learning databases. Neural Information Processing Systems

  • Bai S, Lin Y, Lv Y, Chen J, Wang C (2021) Kernelized fuzzy rough sets based online streaming feature selection for large-scale hierarchical classification. Appl Intell 51(3):1602–1615

    Google Scholar 

  • Chris HQ, Dubchak I (2001) Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17(4):349–358

    Google Scholar 

  • Chen D, Zhang L, Zhao S, Hu Q, Zhu P (2012) A novel algorithm for finding reducts with fuzzy rough sets. IEEE Trans Fuzzy Syst 20(2):385–389

    Google Scholar 

  • Chen D, Zhao S, Zhang L, Zhang X (2012) Sample pair selection for attribute reduction with rough set. IEEE Trans Knowl Data Eng 24(11):2080–2093

    Google Scholar 

  • Chen H, Li T, Luo C, Horng S, Wang G (2014) A rough set-based method for updating decision rules on attribute values’ coarsening and refining. IEEE Trans knowl Data Engin 26(12):2886–2899

    Google Scholar 

  • Chen D, Yang Y, Dong Z (2016) An incremental algorithm for attribute reduction with variable precision rough sets. Appl Soft Comput 45:129–149

    Google Scholar 

  • Cheng Y, Zhang Q, Wang G, Hu B (2020) Optimal scale selection and attribute reduction in multi-scale decision tables based on three-way decision. Inf Sci 541:36–59

    MathSciNet  Google Scholar 

  • Deng J, Dong W, Socher W, Li LJ, Li K, Li FF (2009) Imagenet: A large-scale hierarchical image database. IEEE Conf Comput Vision Pattern Recog 2009:248–255

    Google Scholar 

  • Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17:191–209

    MATH  Google Scholar 

  • Deng J, Zhan J, Ding W, Liu P, Pedrycz W (2022) A novel prospect-theory-based three-way decision methodology in multi-scale information systems. Artif Intell Review. https://doi.org/10.1007/s10462-022-10339-6

    Article  Google Scholar 

  • Escalante HJ (2010) The segmented and annotated IAPR TC-12 benchmark. Comput Vis Image Und 114(4):419–428

    Google Scholar 

  • Fan W, He C, Zeng A, Lin K (2022) An Incremental Approach Based on Hierarchical Classification in Multikernel Fuzzy Rough Sets Under the Variation of Object Set. Int Conf on Intell Comput. https://doi.org/10.1007/978-3-031-13832-4_1

    Article  Google Scholar 

  • Gaussier E, Paliouras G, Androutsopoulos I (2015) Evaluation measures for hierarchical classification: a unified view and novel approaches. Data Mining Knowl Discov 29:820–865

    MathSciNet  MATH  Google Scholar 

  • Hong TP, Liou YL, Wang SL (2009) Fuzzy rough sets with hierarchical quantitative attributes. Expert Syst Appl 36(3):6790–6799

    Google Scholar 

  • Hu Q, Wang Y, Zhou Y, Qian Y, Liang J (2018) Review on hierarchical learning methods for large-scale classification task. Sci Sin Inf 48(5):487–500

    Google Scholar 

  • Huang B, Wu WZ, Yan J, Li H, Zhou X (2020) Inclusion measure-based multi-granulation decision-theoretic rough sets in multi-scale intuitionistic fuzzy information tables. Inf Sci 507:421–448

    Google Scholar 

  • Liu ZT (1999) An incremental arithmetic for the smallest reduction of attributes. Acta Electro Sin 27(11):96–98

    Google Scholar 

  • Lampert CH, Nickisch H, Harmeling S (2009) Learning to detect unseen object classes by between-class attribute transfer. IEEE Conf on Comput Vision and Pattern Recog 2009:951–958

    Google Scholar 

  • Li N, Xie JY (2011) A feature subset selection algorithm based on neighborhood rough set for incremental updating datasets. Comput Technol Dev 21(11):149–152

    Google Scholar 

  • Liang J, Wang F, Dang C, Qian Y (2012) A group incremental approach to feature selection applying rough set technique. IEEE Trans on Knowl and Data Eng 26(2):294–308

    Google Scholar 

  • Li DP, Ju Y, Zou Q (2016) Protein folds prediction with hierarchical structured svm. Curr Proteomics 13(2):79–85

    Google Scholar 

  • Li F, Hu BQ (2017) A new approach of optimal scale selection to multi-scale decision tables. Inf Sci 381:193–208

    Google Scholar 

  • Luo C, Li T, Chen H, Fujita H, Zhang Y (2018) Incremental rough set approach for hierarchical multicriteria classification. Inf Sci 429:72–87

    MathSciNet  MATH  Google Scholar 

  • Li W, Li J, Huang J, Dai W, Zhang X (2021) A new rough set model based on multi-scale covering. Int J Mach Learn Cyb 12(1):243–256

    Google Scholar 

  • Liu X, Zhou Y, Zhao H (2021) Robust hierarchical feature selection driven by data and knowledge. Inf Sci 551:341–357

    MathSciNet  MATH  Google Scholar 

  • Li S, Yang J, Wang G, Zhang Q, Hu J (2022) Granularity Selection for Hierarchical Classification Based on Uncertainty Measure. IEEE Trans on Fuzzy Syst 30(11):4841–4855

    Google Scholar 

  • Lin Y, Liu H, Zhao H, Hu Q, Zhu X, Wu X (2022) Hierarchical Feature Selection Based on Label Distribution Learning. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2022.3177246

    Article  Google Scholar 

  • Morsi NN, Yakout M (1998) Axiomatics for fuzzy rough sets. Fuzzy Sets Syst 100(1):327–342

    MathSciNet  MATH  Google Scholar 

  • Qian W, Shu W, Zhang C (2016) Feature selection from the perspective of knowledge granulation in dynamic set-valued information system. J Inf Sci Eng 32(3):783–798

    MathSciNet  Google Scholar 

  • Qiu Z, Zhao H (2022) A fuzzy rough set approach to hierarchical feature selection based on Hausdorff distance. Appl Intell 52:11089–11102

    Google Scholar 

  • Struyf J, Dzeroski S, Blockeel H, Clare A (2005) Hierarchical multi classification with predictive clustering trees in functional genomics. Springer, Cham

    Google Scholar 

  • Shu W, Shen H (2014) Updating attribute reduction in incomplete decision systems with the variation of attribute set. Int J Approx Reason 55(3):867–884

    MathSciNet  MATH  Google Scholar 

  • Shu W, Shen H (2014) Incremental feature selection based on rough set in dynamic incomplete data. Pattern Recognit 47(12):3890–3906

    Google Scholar 

  • She Y, Li J, Yang H (2015) A local approach to rule induction in multi-scale decision tables. Knowl Based Syst 89:398–410

    Google Scholar 

  • Song J, Zhang P, Qin S, Gong J (2015) A method of the feature selection in hierarchical text classification based on the category discrimination and position information. In Proc Int Conf Ind Informat Comput Technol Intell Technol Ind Inf Integration 2015:132–135

    Google Scholar 

  • Sohn K (2016) Improved deep metric learning with multi-class n-pair loss objective. Adv Neural Inf Process Syst 29:1857–1865

    MathSciNet  Google Scholar 

  • She Y, Qian Z, He X, Wang J, Qian T, Zheng W (2021) On generalization reducts in multi-scale decision tables. Inf Sci 555:104–124

    MathSciNet  MATH  Google Scholar 

  • She Y, Zhao Z, Hu M, Zheng W, He X (2021) On selection of optimal cuts in complete multi-scale decision tables. Artif Intell Review 54(8):6125–6148

    Google Scholar 

  • She Y, Wu J, He X (2022) Research on feature selection algorithm of hierarchical classification based on sample pair selection. J Kunming Univ Sci Technol 47(5):92–102 ((in Chinese))

    Google Scholar 

  • Tuo Q, Zhao H, Hu Q (2019) Hierarchical feature selection with subtree based graph regularization. Knowl-Based Syst 163:996–1008

    Google Scholar 

  • Wu WZ, Leung Y (2011) Theory and applications of granular labelled partitions in multi-scale decision tables. Inf Sci 181(18):3878–3897

    MATH  Google Scholar 

  • Wang F, Liang J, Dang C (2013) Attribute reduction for dynamic data sets. Appl Soft Comput 13(1):676–689

    Google Scholar 

  • Wu WZ, Leung Y (2013) Optimal scale selection for multi-scale decision tables. Int J Approx Reason 54(8):1107–1129

    MathSciNet  MATH  Google Scholar 

  • Wu XD, Zhu XQ, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans on Knowl and Data Eng 26(1):9–107

    Google Scholar 

  • Wu WZ, Qian YH, Li TJ, Gu SM (2017) On rule acquisition in incomplete multi-scale decision tables. Inf Sci 378:282–302

    MathSciNet  MATH  Google Scholar 

  • Wang Y, Wang Z, Hu Q, Zhou Y, Su H (2021) Hierarchical semantic risk minimization for large-scale classification. IEEE Trans Cyber 53(9):9546–9558

    Google Scholar 

  • Wang Y, Hu Q, Chen H, Qian Y (2022) Uncertainty instructed multi-granularity decision for large-scale hierarchical classification. Inf Sci 586:644–661

    Google Scholar 

  • Yang Y, Chen D, Wang H (2016) Active sample selection based incremental algorithm for attribute reduction with rough sets. IEEE Trans Fuzzy Syst 25(4):825–838

    Google Scholar 

  • Yang Y, Chen D, Wang H, Wang X (2018) Incremental perspective for feature selection based on fuzzy rough sets. IEEE Trans on Fuzzy Syst 26(3):1257–1273

    Google Scholar 

  • Yang X, Li Y, Liu D (2021) Hierarchical fuzzy rough approximations with three-way multi-granularity learning. IEEE Trans on Fuzzy Syst 30(9):3486–3500

    Google Scholar 

  • Zhao H, Hu Q, Zhu P, Wang Y, Wang P (2009) A recursive regularization based feature selection framework for hierarchical classification. IEEE Trans Knowl Data Eng 33(7):2833–2846

    Google Scholar 

  • Zeng A, Li T, Liu D, Zhang J (2015) Chen H (2015) A fuzzy rough set approach for incremental feature selection on hybrid information systems. Fuzzy Sets Syst 258:39–60

    MATH  Google Scholar 

  • Zhao S, Han Y, Zou Q, Hu Q (2016) Hierarchical support vector machine based structural classification with fused hierarchies. Neurocomputing 214:86–92

    Google Scholar 

  • Zhao H, Wang P, Hu Q, Zhu P (2019) Fuzzy rough set based feature selection for large-scale hierarchical classification. IEEE Trans on Fuzzy Syst 27(10):1891–1903

    Google Scholar 

  • Zhan J, Xu W (2020) Two types of coverings based multigranulation rough fuzzy sets and applications to decision making. Artif Intell Review 53(1):167–198

    Google Scholar 

  • Zhang Q, Cheng Y, Zhao F, Wang G, Xia S (2021) Optimal scale combination selection integrating three-way decision with hasse diagram. IEEE Trans Neural Netw Learn Syst 33(8):3675–3689

    MathSciNet  Google Scholar 

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Acknowledgements

The authors are very thankful to editors and three referees for their suggestive reports and valuable comments which are conducive to enhancing the presentation of the paper. This work was in part by the National Nature Science Foundation of China under Grants (Nos. 61976244 and 12001422), the Nature Science Foundation of Shaanxi Province under Grants (Nos. 2021JQ-580 and 2023-JC-YB-597)

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Authors and Affiliations

  1. College of Science, Xi’an Shiyou University, Xi’an, 710065, China

    Yanhong She & Xiaoli He

  2. College of Computer Science, Xi’an Shiyou University, Xi’an, 710065, China

    Jinlan Wu

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  1. Yanhong She
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  2. Jinlan Wu
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  3. Xiaoli He
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Contributions

YS: Conceptualization, Methodology, Investigation, Writing-original draft. JW: Methodology, Investigation, Writing—original draft. XH: Writing—Reviewing and Editing.

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Correspondence to Yanhong She.

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She, Y., Wu, J. & He, X. An incremental approach to hierarchical feature selection by applying fuzzy rough set technique. Artif Intell Rev 56 (Suppl 2), 2571–2598 (2023). https://doi.org/10.1007/s10462-023-10584-3

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