A NOVEL EXTENSION METHOD OF VPFRS MODE FOR ATTRIBUTE REDUCTION PROBLEM IN NUMERICAL DECISION TABLES

S. Bashir, I. U. Khattak, A. Khan, F. H. Khan, A. Gani, and M. Shiraz, “A Novel Feature

Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded

Approaches,” Complexity, vol. 2022, pp. 1–12, 2022.

L. Meenachi and S. Ramakrishnan, “Differential evolution and ACO based global optimal feature

selection with fuzzy rough set for cancer data classification,” Soft Computing, vol. 24, no. 24,

pp. 18 463–18 475, 2020.

S. Ahlawat and R. Rishi, “A Genetic Algorithm Based Feature Selection for Handwritten Digit

Recognition,” Recent Patents on Computer Science, vol. 12, no. 4, pp. 304–316, 2018.

H. huang Zhao, H. Liu, H. huang Zhao, and H. Liu, “Multiple classifiers fusion and CNN feature

extraction for handwritten digits recognition,” Granular Computing, vol. 5, no. 3, pp. 411–418,

L. Sun, S. Fu, and F. Wang, “Decision tree SVM model with Fisher feature selection for speech

emotion recognition,” Eurasip Journal on Audio, Speech, and Music Processing, vol. 2019, no. 1,

S. Yildirim, Y. Kaya, and F. Kılı¸c, “A modified feature selection method based on metaheuristic

algorithms for speech emotion recognition,” Applied Acoustics, vol. 173, 2021.

G. Ansari, T. Ahmad, and M. N. Doja, “Spam review classification using ensemble of global

and local feature selectors,” Cybernetics and Information Technologies, vol. 18, no. 4, pp. 29–42,

H. Mohammadzadeh and F. S. Gharehchopogh, “A novel hybrid whale optimization algorithm

with flower pollination algorithm for feature selection: Case study Email spam detection,” Computational Intelligence, vol. 37, no. 1, pp. 176–209, 2021.

A. J. Fern´andez-Garc´ıa, L. Iribarne, A. Corral, J. Criado, and J. Z. Wang, “A recommender

system for component-based applications using machine learning techniques,” Knowledge-Based

Systems, vol. 164, pp. 68–84, 2019.

B. Saravanan, V. Mohanraj, and J. Senthilkumar, “A fuzzy entropy technique for dimensionality

reduction in recommender systems using deep learning,” Soft Computing, vol. 23, no. 8, pp.

–2583, 2019.

Z. Pawlak, “Rough sets,” International Journal of Computer & Information Sciences, vol. 11,

no. 5, pp. 341–356, 1982.

Y. Li, M. Cai, J. Zhou, and Q. Li, “Accelerated multi-granularity reduction based on neighborhood rough sets,” Applied Intelligence, vol. 52, no. 15, pp. 17 636–17 651, 2022.

X. Yang, H. Chen, T. Li, J. Wan, and B. Sang, “Neighborhood rough sets with distance metric

learning for feature selection,” Knowledge-Based Systems, vol. 224, no. 107076, p. 107076, 2021.

J. Liu, Y. Lin, J. Du, H. Zhang, Z. Chen, and J. Zhang, “ASFS: A novel streaming feature

selection for multi-label data based on neighborhood rough set,” Applied Intelligence, 2022.

Q. Hu, D. Yu, and Z. Xie, “Neighborhood classifiers,” Expert Systems with Applications, vol. 34,

no. 2, pp. 640–649, 2008.

D. Liu and J. Li, “Safety monitoring data classification method based on wireless rough network

of neighborhood rough sets,” Safety Science, vol. 118, pp. 282–296, 2019.

J. Ye, J. Zhan, W. Ding, and H. Fujita, “A novel fuzzy rough set model with fuzzy neighborhood

operators,” Information Sciences, vol. 544, pp. 266–297, 2021.

P. Zhang, T. Li, G. Wang, C. Luo, H. Chen, J. Zhang, D. Wang, and Z. Yu, “Multi-source

information fusion based on rough set theory: A review,” pp. 100–107, 2021.

D. Dubois and H. Prade, “Rough fuzzy sets and fuzzy rough sets,” International Journal of

General Systems, vol. 17, no. 2-3, pp. 191–209, 1990.

J. He, L. Qu, Z. Wang, Y. Chen, D. Luo, and C. F. Wen, “Attribute reduction in an incomplete

categorical decision information system based on fuzzy rough sets,” Artificial Intelligence Review,

vol. 55, no. 7, pp. 5313–5348, 2022.

R. K. Huda and H. Banka, “Efficient feature selection methods using PSO with fuzzy rough set

as fitness function,” Soft Computing, vol. 26, no. 5, pp. 2501–2521, 2022.

N. L. Giang, L. H. Son, T. T. Ngan, T. M. Tuan, H. T. Phuong, M. Abdel-Basset, A. R. L. De

Macedo, and V. H. C. De Albuquerque, “Novel Incremental Algorithms for Attribute Reduction

from Dynamic Decision Tables Using Hybrid Filter-Wrapper with Fuzzy Partition Distance,”

IEEE Transactions on Fuzzy Systems, vol. 28, no. 5, pp. 858–873, 2020.

Z. Qiu and H. Zhao, “A fuzzy rough set approach to hierarchical feature selection based on

Hausdorff distance,” Applied Intelligence, vol. 52, no. 10, pp. 11 089–11 102, 2022.

P. Liang, D. Lei, K. S. Chin, and J. Hu, “Feature selection based on robust fuzzy rough sets

using kernel-based similarity and relative classification uncertainty measures,” Knowledge-Based

Systems, vol. 255, no. 109795, p. 109795, 2022.

P. Jain, A. K. Tiwari, and T. Som, “A fitting model based intuitionistic fuzzy rough feature

selection,” Engineering Applications of Artificial Intelligence, vol. 89, no. 103421, p. 103421,

A. Tan, W. Z. Wu, Y. Qian, J. Liang, J. Chen, and J. Li, “Intuitionistic Fuzzy Rough Set-Based

Granular Structures and Attribute Subset Selection,” IEEE Transactions on Fuzzy Systems,

vol. 27, no. 3, pp. 527–539, 2019.

T. T. Nguyen, N. L. Giang, D. T. Tran, T. T. Nguyen, H. Q. Nguyen, A. V. Pham, and T. D.

Vu, “A novel filter-wrapper algorithm on intuitionistic fuzzy set for attribute reduction from

decision tables,” International Journal of Data Warehousing and Mining, vol. 17, no. 4, pp.

–100, 2021.

W. Ziarko, “Variable precision rough set model,” Journal of Computer and System Sciences,

vol. 46, no. 1, pp. 39–59, feb 1993.

S. Zhao, E. C. Tsang, and D. Chen, “The model of fuzzy variable precision rough sets,” IEEE

Transactions on Fuzzy Systems, vol. 17, no. 2, 2009.

Y. Chen and Y. Chen, “Feature subset selection based on variable precision neighborhood rough

sets,” International Journal of Computational Intelligence Systems, vol. 14, no. 1, 2021.