Abstract
Multi-criterion feature ranking algorithms can ease the difficulty on selecting appropriate ranking criterion caused by single-ranking algorithms, and improve the reliability of feature ranking results. However, the issue of conflict between different single-ranking algorithms is often overlooked. By treating this task as a search and optimization process, it is possible to use the D-S theory and evidence conflict to reduce conflicts between different single-criterions and improve the stability of feature evaluation. This work presents a new multi-criterion feature ranking algorithm based on D-S theory and evidence conflict theory combining different criteria improving classification performance of feature selection results. Comparison between the new algorithm and Borda Count, Fuzzy Entropy, Fisher’s Ratio and Representation Entropy methods are done on train fault dataset. The obtained results from the experiment demonstrate that the new algorithm has highest classification accuracy than the other four criterions on all cases considered.
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References
Abdulla, W.H., Kasabov, N.: Reduced feature-set based parallel CHMM speech recognition systems. Inf. Sci. 156(1), 21–38 (2003)
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. pp. 325–339 (1967)
Li, B., Wang. B., Wei. J., Huang, Y., Guo, Z.: An efficient combination rule of evidence theory. J. Data Acquisition Proc. (2002)
Luukka, P.: Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst. Appl. 38(4), 4600–4607 (2011)
Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)
Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507–2517 (2007)
Schubert, J.: Conflict management in Dempster-Shafer theory using the degree of falsity. Int. J. Approximate Reasoning 52(3), 449–460 (2011)
Shafer, G.: A Mathematical Theory of Evidence, vol. 1. Princeton University Press, Princeton (1976)
Smets, P.: Analyzing the combination of conflicting belief functions. Inf. Fusion 8(4), 387–412 (2007)
Song, Q., Ni, J., Wang, G.: A fast clustering-based feature subset selection algorithm for high-dimensional data. Knowl. Data Eng. IEEE Trans. 25(1), 1–14 (2013)
Sun, Q., Xiu qing, Y.E., Wei kang, G.U.: A new combination rules of evidence theory. Acta Electron. Sin. (2000)
Sun, X., Liu, Y., Xu, M., Chen, H., Han, J., Wang, K.: Feature selection using dynamic weights for classification. Knowl. Based Syst. 37, 541–549 (2013)
Van Erp, M., Schomaker, L.: Variants of the Borda count method for combining ranked classifier hypotheses. In: In The Seventh International Workshop on Frontiers in Handwriting Recognition. 2000. Amsterdam Learning Methodology Inspired By Human’s Intelligence Bo Zhang, Dayong Ding, and Ling Zhang (2000)
Yan, W.: Fusion in multi-criterion feature ranking. In: IEEE International Conference on Information Fusion, 10th July 2007, pp. 1–6, (2007)
Yang, F., Mao, K.Z.: Robust feature selection for microarray data based on multicriterion fusion. IEEE/ACM Trans. Comput. Biol. Bioinfor. (TCBB) 8(4), 1080–1092 (2011)
Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004)
Zhou, Z., Xu, X.B., Wen, C.L., Lv, F.: An optimal method for combining conflicting evidences. Acta Autom. Sin. 38(6), 976–985 (2012)
Zhu, J., Fei, Z.: Feature selection for high-dimensional and small-sized data based on multi-criterion fusion. J. Convergence Inf. Technol. 7(19) (2012)
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Du, J., Jin, W., Cai, Z., Zhu, F., Wu, Z. (2017). A New Feature Evaluation Algorithm and Its Application to Fault of High-Speed Railway. In: Lu, H. (eds) Proceedings of the Second International Conference on Intelligent Transportation. ICIT 2016. Smart Innovation, Systems and Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-10-2398-9_1
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DOI: https://doi.org/10.1007/978-981-10-2398-9_1
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