Abstract
Classifier belief represents the confidence of a classifier making judgment about a special instance. Based on classifier belief, we propose an approach to realize classifier belief optimization. Through enriching prior knowledge and thus reducing the scope of candidate classes, our approach improves classification accuracy. A feature perturbation strategy containing an objective optimization is developed to automatically generate labeled instances. Moreover, we propose a classifier consensus strategy (CCS) for classifier optimization. CCS enables a given classifier to take full advantage of the test data to enrich prior knowledge. Experiments on three benchmark datasets and three classical classifiers justify the validity of the proposed approach. We improve the classification accuracy of a linear SVM by 6%.
This work was supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (20XNA031).
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References
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2007)
Bonyadi, M.R., Michalewicz, Z.: Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm. IEEE Trans. Evol. Comput. 20(3), 370–385 (2016)
Bundy, A., Wallen, L.: Dempster-Shafer Theory. Springer, Heidelberg (1984)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)
Chapelle, O., Schölkopf, B., Zien, A.: Semi-supervised learning. In: PAKDD, pp. 588–595 (2006)
Custódio, A.L., Rocha, H., Vicente, L.N.: Incorporating minimum Frobenius norm models in direct search. Comput. Optim. Appl. 46(2), 265–278 (2010). https://doi.org/10.1007/s10589-009-9283-0
Fu, Z., Lu, Z., Ip, H.H.S., Peng, Y., Lu, H.: Symmetric graph regularized constraint propagation. In: AAAI, pp. 350–355 (2011)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Hall, P., Park, B.U., Samworth, R.J.: Choice of neighbor order in nearest-neighbor classification. Ann. Stat. 36(5), 2135–2152 (2008)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Hinton, G.E., Salakhutdinov, R.R.: A better way to pretrain deep boltzmann machines. In: NIPS, pp. 2447–2455 (2012)
Kostina, E.A., Prischepova, S.V.: A new algorithm for minimax and l1-norm optimization. Optimization 44(3), 263–289 (1998)
Lauer, F., Suen, C.Y., Bloch, G.: A trainable feature extractor for handwritten digit recognition. Pattern Recogn. 40(6), 1816–1824 (2007)
Lu, Z., Wang, L., Wen, J.R.: Direct semantic analysis for social image classification. In: AAAI, pp. 1258–1264 (2014)
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: Proceedings of ICVGIP. Citeseer (2008)
Quost, B., Denœux, T., Masson, M.H.: Pairwise classifier combination using belief functions. Pattern Recogn. Lett. 28(5), 644–653 (2007)
Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: CVPR Workshops, pp. 806–813 (2014)
Shen, B., Liu, B.D., Wang, Q., Fang, Y., Allebach, J.P.: SP-SVM: large margin classifier for data on multiple manifolds. In: AAAI, pp. 2965–2971 (2015)
Skinner, B.: The Technology of Teaching. Appleton Century Crofts, New York (1968)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML, pp. 1096–1103 (2008)
Wang, J., Shen, X., Pan, W.: On transductive support vector machines. Contemp. Math. 443, 7–20 (2007)
Zhang, H., et al.: ResNeSt: split-attention networks. arXiv preprint arXiv:2004.08955 (2020)
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Yang, G., Li, X. (2021). Classifier Belief Optimization for Visual Categorization. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12572. Springer, Cham. https://doi.org/10.1007/978-3-030-67832-6_46
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