Abstract
Competitive-collaborative representation based classification (CCRC) has been widely used in pattern recognition and machine learning due to its simplicity, effectiveness, and low complexity. However, its performance is highly dependent on the data distribution. When addressing imbalanced classification issue, its classification results usually tend towards the majority classes. To solve this deficiency, a class weight learning algorithm is introduced into the framework of CCRC for imbalanced classification. The weight of each class is adaptively generated according to the representation ability of each class of training samples, in which the minority classes can be given larger weights. Our proposed model is solved with a closed-form solution and inherits the efficiency property of CCRC. Extensive experimental results show that our model outperforms the commonly used imbalanced classification methods.
Supported by the National Natural Science Foundation of China under Grant 62106233 and 62106068, by the Science and Technology Research Project of Henan Province under Grant 222102210058, 222102210027, and 222102210219, and by the Grant KFJJ2020104, 2019BS032, 2020BSJJ027, and 202210463023, 202210463024.
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References
Barua, S., Islam, M.M., Yao, X., Murase, K.: MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans. Knowl. Data Eng. 26(2), 405–425 (2013)
Batista, G.E., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor. Newsl. 6(1), 20–29 (2004)
Cao, P., Liu, X., Zhang, J., Zhao, D., Huang, M., Zaiane, O.: l(2,1) norm regularized multi-kernel based joint nonlinear feature selection and over-sampling for imbalanced data classification. Neurocomputing 234, 38–57 (2017)
Cao, P., Zhao, D., Zaiane, O.: An optimized cost-sensitive svm for imbalanced data learning. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 280–292. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37456-2_24
Castro, C.L., Braga, A.P.: Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 24(6), 888–899 (2013)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique (2011)
Guo, H., Liu, H., Wu, C., Zhi, W., Xiao, Y., She, W.: Logistic discrimination based on G-mean and F-measure for imbalanced problem. J. Intell. Fuzzy Syst. 31, 1155–1166 (2016)
He, H., Garcia, E.A.: Learning from imbalanced data (2008)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
He, H., Yang, B., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on IEEE World Congress on Computational Intelligence Neural Networks, IJCNN 2008 (2008)
Hernandez, J., Carrasco-Ochoa, J.A., Trinidad, J.F.M.: An empirical study of oversampling and undersampling for instance selection methods on imbalance datasets. In: CIARP (1) (2013)
Huang, C., Li, Y., Loy, C.C., Tang, X.: Learning deep representation for imbalanced classification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 5375–5384. IEEE Computer Society (2016). https://doi.org/10.1109/CVPR.2016.580
Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878–887. Springer, Heidelberg (2005). https://doi.org/10.1007/11538059_91
Jin, J., Li, Y., Chen, C.: Pattern classification with corrupted labeling via robust broad learning system. IEEE Trans. Knowl. Data Eng. 34(10), 4959–4971 (2021)
Jin, J., Li, Y., Yang, T., Zhao, L., Duan, J., Chen, C.P.: Discriminative group-sparsity constrained broad learning system for visual recognition. Inf. Sci. 576, 800–818 (2021)
Khan, M., Arif, R.B., Siddique, M., Oishe, M.R.: Study and observation of the variation of accuracies of KNN, SVM, LMNN, ENN algorithms on eleven different datasets from UCI machine learning repository (2018)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence (1995)
Li, Y., Zhang, L., Qian, T.: 2D partial unwinding-a novel non-linear phase decomposition of images. IEEE Trans. Image Process. 28(10), 4762–4773 (2019)
Li, Y., Jin, J., Zhao, L., Wu, H., Sun, L., Chen, C.L.P.: A neighborhood prior constrained collaborative representation for classification. Int. J. Wavel. Multiresolut. Inf. Process. 19, 2050073 (2020)
Liu, X., Wu, J., Zhou, Z., Member, S.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B 39, 539–550 (2008)
Shu, T., Zhang, B., Tang, Y.Y.: Sparse supervised representation-based classifier for uncontrolled and imbalanced classification. IEEE Trans. Neural Netw. Learn. Syst. 31, 2847–2856 (2018)
Wang, H., Wang, X., Chen, C., Cheng, Y.: Hyperspectral image classification based on domain adaptation broad learning. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 13(99), 3006–3018 (2020)
Cheng, F., Zhang, J., Wen, C., Liu, Z., Li, Z.: Large cost-sensitive margin distribution machine for imbalanced data classification. Neurocomputing 224, 45–57 (2017)
Yang, P., Yoo, P.D., Fernando, J., Zhou, B.B., Zhang, Z., Zomaya, A.Y.: Sample subset optimization techniques for imbalanced and ensemble learning problems in bioinformatics applications. IEEE Trans. Cybern. 44(3), 445–455 (2013)
Yang, X., Kuang, Q., Zhang, W., Zhang, G.: AMDO: an over-sampling technique for multi-class imbalanced problems. IEEE Trans. Knowl. Data Eng. 30(9), 1672–1685 (2017)
Yuan, H., Li, X., Xu, F., Wang, Y., Lai, L.L., Tang, Y.Y.: A collaborative-competitive representation based classifier model. Neurocomputing 275, 627–635 (2018)
Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning 101, 229–242 (2013). https://doi.org/10.1016/j.neucom.2012.08.010
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Li, Y., Wang, S., Jin, J., Philip Chen, C.L. (2022). Weighted Competitive-Collaborative Representation Based Classifier for Imbalanced Data Classification. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_38
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