Analysis of Rank Aggregation Techniques for Rank Based on the Feature Selection Technique

Article Sidebar

A Borda Count rank aggregation example
PDF
Published: Mar 11, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i3s.6160
Keywords:
CS, Mutual Information, Information Gain, Feature Selection, Relief from Rank Aggregation, Gain Ratio, Symmetrical Uncertainty

Main Article Content

Alisha Sikri
Research Scholar, Department of Computer Science and Engineering, SRM University, Delhi-NCR, India.
N. P. Singh
Professor, School of Business Management and Commerce, MVN University (NCR), Palwal, Haryana
Surjeet Dalal
Professor, Department of Computer Science and Engineering, Amity University, Gurugram, Haryana.

Abstract

In order to improve classification accuracy and lower future computation and data collecting costs, feature selection is the process of choosing the most crucial features from a group of attributes and removing the less crucial or redundant ones. To narrow down the features that need to be analyzed, a variety of feature selection procedures have been detailed in published publications. Chi-Square (CS), IG, Relief, GR, Symmetrical Uncertainty (SU), and MI are six alternative feature selection methods used in this study. The provided dataset is aggregated using four rank aggregation strategies: "rank aggregation," "Borda Count (BC) methodology," "score and rank combination," and "unified feature scoring" based on the outcomes of the six feature selection method (UFS). These four procedures by themselves were unable to generate a clear selection rank for the characteristic. To produce different ranks of traits, this ensemble of aggregating ranks is carried out. For this, the bagging method of majority voting was applied.

Article Details

How to Cite
Sikri, A. ., Singh, N. P. ., & Dalal, S. . (2023). Analysis of Rank Aggregation Techniques for Rank Based on the Feature Selection Technique. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 95–108. https://doi.org/10.17762/ijritcc.v11i3s.6160
Issue
Vol. 11 No. 3s (2023)
Section
Articles

References

Bahassine, S., Madani, A., Al-Sarem, M., &Kissi, M. “Feature selection using an improved Chi-square for Arabic text classification”. Journal of King Saud University-Computer and Information Sciences, vol.32, pp.225-231, 2020.

Rachburee, Nachirat, and Wattana Punlumjeak. "A comparison of feature selection approach between greedy, IG-ratio, Chi-square, and mRMR in educational mining." In 2015 7th international conference on information technology and electrical engineering (ICITEE), pp. 420-424. IEEE, 2015.

DeepaLakshmi, S., & Velmurugan, T. “Empirical study of feature selection methods for high dimensional data”. Indian Journal of Science and Technology, 9(39), pp. 1-6, 2016.

Dahiya, Shashi, S. Handa, and N. Singh. "A rank aggregation algorithm for ensemble of multiple feature selection techniques in credit risk evaluation." International Journal of Advanced Research in Artificial Intelligence 5, no. 9 (2016): 1-8.

Ali, Maqbool, Rahman Ali, Wajahat Ali Khan, Soyeon Caren Han, Jaehun Bang, TaehoHur, Dohyeong Kim, Sungyoung Lee, and Byeong Ho Kang. "A data-driven knowledge acquisition system: An end-to-end knowledge engineering process for generating production rules." IEEE Access 6 (2018): 15587-15607.

Ali, M., Ali, S. I., Kim, D., Hur, T., Bang, J., Lee, S., ... & Hussain, M. “uEFS: An efficient and comprehensive ensemble-based feature selection methodology to select informative features”. PloS one, vol. 13, e0202705, 2018.

Lai, H. J., Pan, Y., Tang, Y., & Yu, R. “FSMRank: Feature selection algorithm for learning to rank”. IEEE transactions on neural networks and learning systems, vol.24, pp.940-952, 2013.

.Christo, V. R., Khanna. N, H., Minu, B., & Kannan, A. “Correlation-based ensemble feature selection using bioinspired algorithms and classification using backpropagation neural network”. Computational and mathematical methods in medicine, 2019.

Pes, B. “Ensemble feature selection for high-dimensional data: a stability analysis across multiple domains”. Neural Computing and Applications, vol. 32, pp.5951-5973, 2020.

Zheng, W., & Jin, M. “Comparing Feature Selection Methods by Using Rank Aggregation”. In Proc. 16th International Conference on ICT and Knowledge Engineering (ICT&KE), pp. 1-6, IEEE, 2018.

Sulistiani, H., &Tjahyanto, A. “Comparative Analysis of Feature Selection Method to Predict Customer Loyalty”. IPTEK The Journal of Engineering, vol. 3, pp.1-5, 2017.

Li, X., Wang, X., & Xiao, G. “A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications”. Briefings in bioinformatics, vol.20, pp.178-189, 2017.

Zhu, Z., Wang, X., &Qiu, S. “Comparing Rank Aggregation Methods based on Mallows Model”. In Proc. International Conference on Mechatronics Engineering and Information Technology (ICMEIT), Atlantis Press, 2019, pp. 609-616.

Al-Harbi, O. “A comparative study of feature selection methods for dialectal Arabic sentiment classification using support vector machine”. arXiv preprint arXiv:1902.06242, 2019.

Wang, J., Xu, J., Zhao, C., Peng, Y., & Wang, H. “An ensemble feature selection method for high-dimensional data based on sort aggregation”. Systems Science & Control Engineering, 7(2), pp.32-39, 2019.

Lin, S. “Rank aggregation methods”. Wiley Interdisciplinary Reviews: Computational Statistics, 2010, pp. 555-570.

Prati, R. C. “Combining feature ranking algorithms through rank aggregation”. In Proc. International Joint Conference on Neural Networks (IJCNN), pp.1-8. IEEE, 2012.

Kumar, C. Sunil & R. J. Sree. "Application of Ranking Based Attribute Selection Filters to Perform Automated Evaluation of Descriptive Answers Through Sequential Minimal Optimization Models." ICTACT Journal on Soft Computing 5.1, 2014.

Kira,K., & Rendell. L.A. “A Practical approach to Feature Selection”. In Proc. Ninth International Workshop on Machine Learning, pp. 249-256.

Kreer, J. G. (1957). "A question of terminology". IRE Transactions on Information Theory. 3 (3): 208. doi:10.1109/TIT.1957.1057418.

Liu, Y. T., Liu, T. Y., Qin, T., Ma, Z. M., & Li, H. “Supervised rank aggregation”. In Proc. 16th international conference on World Wide Web, pp. 481-490, 2007.

Lin, Y. L., Chakhar, S., & Yang, R. “Sequential Rank Aggregation Method”. In Proc. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3194-3200. IEEE, 2018.

Renda, M. E., &Straccia, U. (2003, March). “Web metasearch: rank vs. score-based rank aggregation methods”. In Proc. ACM symposium on Applied computing, 2003, pp. 841-846.

Najdi, S., Gharbali, A. A., & Fonseca, J. M. “Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study”. Biomedical engineering online, 16(1), pp.1-19, 2017.

Zhang, Y., Zhang, W., Pei, J., Lin, X., Lin, Q., & Li, A. “Consensus-based ranking of multivalued objects: A generalized borda count approach”. IEEE Transactions on Knowledge and Data Engineering, 26(1), pp.83-96, 2012.

Xiao, Y., Deng, Y., Wu, J., Deng, H. Z., & Lu, X. “Comparison of rank aggregation methods based on inherent ability”. Naval Research Logistics (NRL), vol.64(7), pp.556-565, 2017.

Zhao.S, Zhang. Y, Xu.H&Han.T. “Ensemble Classification Based on Feature Selection for Environmental Sound Recognition”. Mathematical Problems in Engineering, 2019.

Bolón-Canedo, V., & Alonso-Betanzos, A. “Ensembles for feature selection: A review and future trends” Information Fusion, vol.52, pp. 1-12, 2019.

Huang.F, Xie.G&Xiao.R. “Research on Ensemble Learning”, In Proc. International Conference on Artificial Intelligence and Computational Intelligence”, IEEE Computer Society, 2009.

Sikri.A, Singh.N.P&Dalal. S. “Impact of feature selection based on aggregation of ensemble techniques on Classification algorithms, 2020a (Unpublished paper).

Sikri.A, Singh.N.P&Dalal. S. “Impact of feature selection on Classification algorithms, 2020b (Unpublished paper).