Abstract
A combination method is an integral part of an ensemble classifier. Existing combination methods determine the combined prediction of a new instance by relying on the predictions made by the majority of base classifiers. This can result in incorrect combined predictions when the majority predict the incorrect class. It has been noted that in group decision-making, the decision by the majority, if lacking consistency in the reasons for the decision provided by its members, could be less reliable than the minority’s decision with higher consistency in the reasons of its members. Based on this observation, in this paper, we propose a new combination method, EBCM, which considers the consistency of the features, i.e. explanations of individual predictions for generating ensemble classifiers. EBCM firstly identifies the features accountable for each base classifier’s prediction, and then uses the features to measure the consistency among the predictions. Finally, EBCM combines the predictions based on both the majority and the consistency of features. We evaluated the performance of EBCM with 16 real-world datasets and observed substantial improvement over existing techniques.
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References
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., Müller, K.R.: How to explain individual classification decisions. JMLR 11, 1803–1831 (2010)
Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49–64 (1996)
Cevikalp, H., Polikar, R.: Local classifier weighting by quadratic programming. IEEE Trans. Neural Netw. 19(10), 1832–1838 (2008)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. JMLR 7, 1–30 (2006)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45014-9_1
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2), 337–407 (2000)
García, S., Fernández, A., Luengo, J., Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput. 13(10), 959 (2009)
Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A rational consensus model in group decision making using linguistic assessments. Fuzzy Sets Syst. 88(1), 31–49 (1997)
Huang, Y.S., Suen, C.Y.: The behavior-knowledge space method for combination of multiple classifiers. In: Proceedings of CVPR, pp. 347–352. IEEE (1993)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)
Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms, 2nd edn. Wiley, New York (2014)
Kuncheva, L.I.: A theoretical study on six classifier fusion strategies. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 281–286 (2002)
Kuncheva, L.I., Bezdek, J.C., Duin, R.P.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognit. 34(2), 299–314 (2001)
Onan, A., Korukoğlu, S., Bulut, H.: A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert. Syst. Appl. 62, 1–16 (2016)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of EMNLP, pp. 79–86. ACL (2002)
Perikos, I., Hatzilygeroudis, I.: Recognizing emotions in text using ensemble of classifiers. Eng. Appl. Artif. Intell. 51, 191–201 (2016)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?”: explaining the predictions of any classifier. In: The 22nd ACM SIGKDD, pp. 1135–1144 (2016)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Comput. Appl. Math. 20, 53–65 (1987)
Tofallis, C.: Add or multiply? a tutorial on ranking and choosing with multiple criteria. INFORMS Trans. Educ. 14(3), 109–119 (2014)
Whitehead, M., Yaeger, L.: Sentiment mining using ensemble classification models. In: Sobh, T. (ed.) Innovations and Advances in Computer Sciences and Engineering, pp. 509–514. Springer, Dordrecht (2010). https://doi.org/10.1007/978-90-481-3658-2_89
Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)
Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)
Yan, Y., Yang, H., Wang, H.: Two simple and effective ensemble classifiers for twitter sentiment analysis. In: 2017 Computing Conference, pp. 1386–1393 (2017)
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We acknowledge the University of South Australia and Data to Decisions CRC (D2DCRC) for partially funding this research.
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Islam, M.Z., Liu, J., Liu, L., Li, J., Kang, W. (2019). Semantic Explanations in Ensemble Learning. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11439. Springer, Cham. https://doi.org/10.1007/978-3-030-16148-4_3
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