IEEE/CAA Journal of Automatica Sinica
Citation: | Z. W. Zhang, S. T. Ye, Y. R. Zhang, W. P. Ding, and H. Wang, “Belief combination of classifiers for incomplete data,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 652–667, Apr. 2022. doi: 10.1109/JAS.2022.105458 |
[1] |
K. Y. Chiang, I. S. Dhillon, and C. J. Hsieh, “Using side information to reliably learn low-rank matrices from missing and corrupted observations,” J. Mach. Learn. Res., vol. 19, no. 1, pp. 3005–3039, Jan. 2018.
|
[2] |
N. Städler, D. J. Stekhoven, and Bühlmann, “Pattern alternating maximization algorithm for missing data in high-dimensional problems,” J. Mach. Learn. Res., vol. 15, no. 1, pp. 1903–1928, Jan. 2014.
|
[3] |
B. Fekade, T. Maksymyuk, M. Kyryk, and M. Jo, “Probabilistic recovery of incomplete sensed data in IoT,” IEEE Internet Things J., vol. 5, no. 4, pp. 2282–2292, Aug. 2018. doi: 10.1109/JIOT.2017.2730360
|
[4] |
J. Pan, C. B. Li, Y. Tang, W. Li, and X. O. Li, “Energy consumption prediction of a CNC machining process with incomplete data,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 987–1000, May 2021. doi: 10.1109/JAS.2021.1003970
|
[5] |
L. Chen, L. Q. Wang, Z. Y. Han, J. Zhao, and W. Wang, “Variational inference based kernel dynamic Bayesian networks for construction of prediction intervals for industrial time series with incomplete input,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1437–1445, Sep. 2020.
|
[6] |
Z. C. Feng, W. He, Z. J. Zhou, X. J. Ban, C. H. Hu, and X. X. Han, “A new safety assessment method based on belief rule base with attribute reliability,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 11, pp. 1774–1785, Nov. 2021. doi: 10.1109/JAS.2020.1003399
|
[7] |
R. C. Merton, “A simple model of capital market equilibrium with incomplete information,” J. Finance, vol. 42, no. 3, pp. 483–510, Jul. 1987. doi: 10.1111/j.1540-6261.1987.tb04565.x
|
[8] |
J. García-Laencina, J. L. Sancho-Gómez, and A. R. Figueiras-Vidal, “Pattern classification with missing data: A review,” Neural Comput. Appl., vol. 19, no. 2, pp. 263–282, Mar. 2010. doi: 10.1007/s00521-009-0295-6
|
[9] |
R. J. A. Little and D. B. Rubin, Statistical Analysis with Missing Data. 3rd ed. New York, USA: Wiley, 2019.
|
[10] |
D. Shen, “Iterative learning control with incomplete information: A survey,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 885–901, Sep. 2018. doi: 10.1109/JAS.2018.7511123
|
[11] |
D. Bertsimas, C. Pawlowski, and Y. D. Zhuo, “From predictive methods to missing data imputation: An optimization approach,” J. Mach. Learn. Res., vol. 18, no. 1, pp. 7133–7171, Jan. 2017.
|
[12] |
D. Williams, X. J. Liao, Y. Xue, L. Carin, and B. Krishnapuram, “On classification with incomplete data,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 3, pp. 427–436, Mar. 2007. doi: 10.1109/TPAMI.2007.52
|
[13] |
Z. Ghahramani and M. I. Jordan, “Supervised learning from incomplete data via an EM approach,” in Proc. 6th Int. Conf. Neural Information Processing Systems, Denver, USA, 1993, pp. 120−127.
|
[14] |
J. R. Quinlan, C4.5: Programs for Machine Learning. Amsterdam, the Netherlands: Elsevier, 2014.
|
[15] |
M. Kuhn and K. Johnson, Applied Predictive Modeling. New York, USA: Springer, 2013.
|
[16] |
G. De'ath and K. E. Fabricius, “Classification and regression trees: A powerful yet simple technique for ecological data analysis,” Ecology, vol. 81, no. 11, pp. 3178–3192, Nov. 2000. doi: 10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2
|
[17] |
Patidar and A. Tiwari, “Handling missing value in decision tree algorithm,” Int. J. Comput. Appl., vol. 70, no. 13, pp. 31–36, May 2013.
|
[18] |
C. Lim, J. H. Leong, and M. M. Kuan, “A hybrid neural network system for pattern classification tasks with missing features,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 4, pp. 648–653, Apr. 2005. doi: 10.1109/TPAMI.2005.64
|
[19] |
K. Pelckmans, J. De Brabanter, J. A. K. Suykens, and B. De Moor, “Handling missing values in support vector machine classifiers,” Neural Netw., vol. 18, no. 5-6, pp. 684–692, Jul-Aug. 2005. doi: 10.1016/j.neunet.2005.06.025
|
[20] |
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proc. 1st Int. Conf. Learning Representations, Scottsdale, USA, 2013.
|
[21] |
S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. 3rd ed. Essex, UK: Pearson Education Limited, 2016.
|
[22] |
L. S. Chan and O. J. Dunn, “The treatment of missing values in discriminant analysis-I. The sampling experiment,” J. Am. Stat. Assoc., vol. 67, no. 338, pp. 473–477, Jun. 1972.
|
[23] |
L. Brás and J. C. Menezes, “Improving cluster-based missing value estimation of DNA microarray data,” Biomol. Eng., vol. 24, no. 2, pp. 273–282, Jun. 2007. doi: 10.1016/j.bioeng.2007.04.003
|
[24] |
J. Luengo, J. A. Sáez, and F. Herrera, “Missing data imputation for fuzzy rule-based classification systems,” Soft Comput., vol. 16, no. 5, pp. 863–881, May 2012. doi: 10.1007/s00500-011-0774-4
|
[25] |
S. G. Liu, J. Zhang, Y. Xiang, and W. L. Zhou, “Fuzzy-based information decomposition for incomplete and imbalanced data learning,” IEEE Trans. Fuzzy Syst., vol. 25, no. 6, pp. 1476–1490, Dec. 2017. doi: 10.1109/TFUZZ.2017.2754998
|
[26] |
B. Muzellec, J. Josse, C. Boyer, and M. Cuturi, “Missing data imputation using optimal transport,” in Proc. 37th Int. Conf. Machine Learning, 2020, pp. 7130−7140.
|
[27] |
D. B. Rubin, Multiple Imputation for Nonresponse in Surveys. New York, USA: John Wiley & Sons Inc., 2004.
|
[28] |
S. van Buuren and K. Groothuis-Oudshoorn, “mice: Multivariate imputation by chained equations in R,” J. Stat. Softw., vol. 45, no. 3, pp. 1–67, Dec. 2011.
|
[29] |
J. Yoon, J. Jordon, and M. Schaar, “GAIN: Missing data imputation using generative adversarial nets,” in Proc. 35th Int. Conf. Machine Learning, Stockholm, Sweden, 2018, pp. 5689−5698.
|
[30] |
D. J. Stekhoven and Bühlmann, “MissForest-non-parametric missing value imputation for mixed-type data,” Bioinformatics, vol. 28, no. 1, pp. 112–118, Jan. 2012. doi: 10.1093/bioinformatics/btr597
|
[31] |
Z. G. Liu, Q. Pan, G. Mercier, and J. Dezert, “A new incomplete pattern classification method based on evidential reasoning,” IEEE Trans. Cybern., vol. 45, no. 4, pp. 635–646, Apr. 2014.
|
[32] |
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Proc. 27th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2672−2680.
|
[33] |
A. Dempster, “Upper and lower probabilities induced by a multivalued mapping,” Ann. Math. Stat., vol. 38, no. 2, pp. 325–339, Apr. 1967. doi: 10.1214/aoms/1177698950
|
[34] |
G. Shafer, A Mathematical Theory of Evidence. Princeton, USA: Princeton University Press, 1976.
|
[35] |
B. Quost, M. H. Masson, and T. Denoeux, “Classifier fusion in the dempster-Shafer framework using optimized t-norm based combination rules,” Int. J. Approx. Reason., vol. 52, no. 3, pp. 353–374, Mar. 2011. doi: 10.1016/j.ijar.2010.11.008
|
[36] |
D. Mercier, G. Cron, T. Denoeux, and M. H. Masson, “Decision fusion for postal address recognition using belief functions,” Expert Syst. Appl., vol. 36, no. 3, pp. 5643–5653, Apr. 2009. doi: 10.1016/j.eswa.2008.06.058
|
[37] |
F. Y. Xiao, “A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion,” Inf. Sci., vol. 514, pp. 462–483, Apr. 2020. doi: 10.1016/j.ins.2019.11.022
|
[38] |
Z. G. Liu, Q. Pan, J. Dezert, J. W. Han, and Y. He, “Classifier fusion with contextual reliability evaluation,” IEEE Trans. Cybern., vol. 48, no. 5, pp. 1605–1618, May 2018. doi: 10.1109/TCYB.2017.2710205
|
[39] |
Smets, “Decision making in the TBM: The necessity of the pignistic transformation,” Int. J. Approx. Reason., vol. 38, no. 2, pp. 133–147, Feb. 2005. doi: 10.1016/j.ijar.2004.05.003
|
[40] |
Y. Leung, N. N. Ji, and J. H. Ma, “An integrated information fusion approach based on the theory of evidence and group decision-making,” Inf. Fusion, vol. 14, no. 4, pp. 410–422, Oct. 2013. doi: 10.1016/j.inffus.2012.08.002
|
[41] |
Z. W. Zhang, H. P. Tian, L. Z. Yan, A. Martin, and K. Zhou, “Learning a credal classifier with optimized and adaptive multiestimation for missing data imputation,” IEEE Trans. Syst., Man, Cybern.: Syst., 2021. doi: 10.1109/TSMC.2021.3090210.
|
[42] |
A. Martin and E. Radoi, “Effective ATR algorithms using information fusion models,” in Proc. 7th Int. Conf. Information Fusion, Stockholm, Sweden, 2004, pp. 161−166.
|
[43] |
T. Denoeux, “Decision-making with belief functions: A review,” Int. J. Approx. Reason., vol. 109, pp. 87–110, Jun. 2019. doi: 10.1016/j.ijar.2019.03.009
|
[44] |
M. H. Masson and T. Denoeux, “ECM: An evidential version of the fuzzy c-means algorithm,” Patt. Recognit., vol. 41, no. 4, pp. 1384–1397, Apr. 2008. doi: 10.1016/j.patcog.2007.08.014
|
[45] |
Z. G. Su and T. Denoeux, “BPEC: Belief-peaks evidential clustering,” IEEE Trans. Fuzzy Syst., vol. 27, no. 1, pp. 111–123, Jan. 2019. doi: 10.1109/TFUZZ.2018.2869125
|
[46] |
F. J. Li, Y. H. Qian, J. T. Wang, and J. Y. Liang, “Multigranulation information fusion: A Dempster-Shafer evidence theory-based clustering ensemble method,” Inf. Sci., vol. 378, pp. 389–409, Feb. 2017. doi: 10.1016/j.ins.2016.10.008
|
[47] |
T. Denoeux, “A k-nearest neighbor classification rule based on Dempster-Shafer theory,” IEEE Trans. Syst.,Man,Cybern., vol. 25, no. 5, pp. 804–813, May 1995. doi: 10.1109/21.376493
|
[48] |
T. Denoeux, “Logistic regression, neural networks and Dempster-Shafer theory: A new perspective,” Knowl.-Based Syst., vol. 176, pp. 54–67, Jul. 2019. doi: 10.1016/j.knosys.2019.03.030
|
[49] |
J. Zhao, R. Xue, Z. N. Dong, D. Y. Tang, and W. H. Wei, “Evaluating the reliability of sources of evidence with a two-perspective approach in classification problems based on evidence theory,” Inf. Sci., vol. 507, pp. 313–338, Jan. 2020. doi: 10.1016/j.ins.2019.08.033
|
[50] |
Z. F. Ma, H. Tian, Z. C. Liu, and Z. W. Zhang, “A new incomplete pattern belief classification method with multiple estimations based on KNN,” Appl. Soft Comput., vol. 90, Article No. 106175, May 2020. doi: 10.1016/j.asoc.2020.106175
|
[51] |
M. L. Seltzer, B. Raj, and R. M. Stern, “A Bayesian classifier for spectrographic mask estimation for missing feature speech recognition,” Speech Commun., vol. 43, no. 4, pp. 379–393, Sep. 2004. doi: 10.1016/j.specom.2004.03.006
|
[52] |
S. J. Choudhury and N. R. Pal, “Imputation of missing data with neural networks for classification,” Knowl.-Based Syst., vol. 182, Article No. 104838, Oct. 2019. doi: 10.1016/j.knosys.2019.07.009
|
[53] |
M. J. D. Powell, “A fast algorithm for nonlinearly constrained optimization calculations,” in Numerical Analysis, G. A. Watson, Ed. Berlin, Heidelberg, Germany: Springer, 1978, pp. 144−157.
|
[54] |
J. Benesty, J. D. Chen, Y. T. Huang, and I. Cohen, “Pearson correlation coefficient,” in Noise Reduction in Speech Processing, I. Cohen, Y. T. Huang, J. D. Chen, and J. Benesty, Eds. Heidelberg, Germany: Springer, 2009, pp. 1−4.
|
[55] |
“Spearman rank correlation coefficient,” in The Concise Encyclopedia of Statistics, New York, USA: Springer, 2008, pp. 502−505.
|
[56] |
M. G. Kendall, Rank Correlation Methods. London: Griffin, 1948.
|
[57] |
Smets, “The combination of evidence in the transferable belief model,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 5, pp. 447–458, May 1990. doi: 10.1109/34.55104
|
[58] |
R. R. Yager, “On the Dempster-Shafer framework and new combination rules,” Inf. Sci., vol. 41, no. 2, pp. 93–137, Mar. 1987. doi: 10.1016/0020-0255(87)90007-7
|
[59] |
D. Dubois and H. Prade, “Representation and combination of uncertainty with belief functions and possibility measures,” Comput. Intell., vol. 4, no. 3, pp. 244–264, Sep. 1988. doi: 10.1111/j.1467-8640.1988.tb00279.x
|
[60] |
J. Dezert and A. Tchamova, “On the validity of dempster's fusion rule and its interpretation as a generalization of Bayesian fusion rule,” Int. J. Intell. Syst., vol. 29, no. 3, pp. 223–252, Mar. 2014. doi: 10.1002/int.21638
|
[61] |
Y. M. Yang, “An evaluation of statistical approaches to text categorization,” Inf. Retrieval, vol. 1, no. 1–2, pp. 69–90, Apr. 1999.
|
[62] |
T. Cover and Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, Jan. 1967. doi: 10.1109/TIT.1967.1053964
|
[63] |
J. Twisk, M. de Boer, W. de Vente, and M. Heymans, “Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis,” J. Clin. Epidemiol., vol. 66, no. 9, pp. 1022–1028, Sep. 2013. doi: 10.1016/j.jclinepi.2013.03.017
|