Abstract
Imbalanced datasets often result in poor predictive model performance. To address this, minority class sample expansion is used, but two challenges remain. The first is to use algorithms to learn the main features of minority class samples, and the second is to differentiate the generated data from the majority class samples. To tackle these challenges in binary classification, we propose the Discriminant-Autoencoder (D-AE) algorithm. It has two mechanisms based on our insights. Firstly, an autoencoder is used to learn the main features of minority class samples by reconstructing the data with added noise. Secondly, a discriminator is trained on the raw data to distinguish the generated data from the majority class samples. Our proposed loss function, Discriminant-\(L_\theta \), balances the discriminant and reconstruction losses. Results from experiments on three datasets show that D-AE outperforms baseline algorithms and improves dataset applicability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akkalakshmi, M., Riyazuddin, Y.M., Revathi, V., Pal, A.: Autoencoder-based feature learning and up-sampling to enhance cancer prediction. Int. J. Future Gener. Commun. Netw. 13(1), 1453–1459 (2020)
AlAmir, M., AlGhamdi, M.: The role of generative adversarial network in medical image analysis: an in-depth survey. ACM Comput. Surv. (CSUR) 55(5), 1–36 (2022)
Anguita, D., Ghio, A., Oneto, L., Parra Perez, X., Reyes Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 437–442 (2013)
Back, K., Crotty, K., Kazempour, S.M., Schwert, G.W.: Validity, tightness, and forecasting power of risk premium bounds. J. Financ. Econ. 144, 732–760 (2022)
Baldi, P.: Boolean autoencoders and hypercube clustering complexity. Des. Codes Crypt. 65(3), 383–403 (2012)
Cetinkunt, S., Donmez, A.: CMAC learning controller for servo control of high precision machine tools. In: 1993 American Control Conference (1993)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. AI Access Found. (1) (2002)
Chen, Z., Zhou, L., Yu, W.: Adasyn-random forest based intrusion detection model (2021)
Cho, K.: Simple sparsification improves sparse denoising autoencoders in denoising highly corrupted images. In: International Conference on Machine Learning (2013)
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018)
Egan, J.P.: Signal detection theory and roc analysis. In: Series in Cognition and Perception. Academic Press, New York (1975)
Gajera, V., Shubham, Gupta, R., Jana, P.K.: An effective multi-objective task scheduling algorithm using min-max normalization in cloud computing. In: 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (2016)
Garcia, S., Derrac, J., Cano, J., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417 (2012)
Gu, S., Yan, J., Xiao, Z., Ning, L., Tech, V.: What are driving users to click ads? User habit, attitude, and commercial intention (2010)
Gupta, A., Anand, A., Hasija, Y.: Recall-based machine learning approach for early detection of cervical cancer. In: 2021 6th International Conference for Convergence in Technology (I2CT) (2021)
Han, B., Wang, X., Ji, S., Zhang, G., He, J.: Data-enhanced stacked autoencoders for insufficient fault classification of machinery and its understanding via visualization. IEEE Access 8(99), 67790–67798 (2020)
Han, H., Wang, W.Y., Mao, B.H.: Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I (2005)
He, H., Yang, B., Garcia, E.A., Li, S.: Adasyn: adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008 (IEEE World Congress on Computational Intelligence) (2008)
Kachuee, M., Fazeli, S., Sarrafzadeh, M.: ECG heartbeat classification: a deep transferable representation. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 443–444. IEEE (2018)
Komoto, K., Nakatsuka, S., Aizawa, H., Kato, K., Kobayashi, H., Banno, K.: A performance evaluation of defect detection by using denoising autoencoder generative adversarial networks. In: 2018 International Workshop on Advanced Image Technology (IWAIT), pp. 1–4. IEEE (2018)
Lobo, J.M., Jiménez-Valverde, A., Real, R.: AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17(2), 145–151 (2008)
Lu, C., Lin, S., Liu, X., Shi, H.: Telecom fraud identification based on adasyn and random forest. In: 2020 5th International Conference on Computer and Communication Systems (ICCCS) (2020)
Lu, X., Tsao, Y., Matsuda, S., Hori, C.: Speech enhancement based on deep denoising autoencoder. In: Interspeech, vol. 2013, pp. 436–440 (2013)
Mehta, J., Majumdar, A.: Rodeo: robust de-aliasing autoencoder for real-time medical image reconstruction. Pattern Recognit. J. Pattern Recognit. Soc. 63, 499–510 (2017)
Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18(4), 1064 (2018)
Mqadi, N.M., Naicker, N., Adeliyi, T.: Solving misclassification of the credit card imbalance problem using near miss. Math. Probl. Eng. Theory Methods Appl. (2021-Pt.32) (2021)
Naseriparsa, M., Kashani, M.: Combination of PCA with smote resampling to boost the prediction rate in lung cancer dataset. Found. Comput. Sci. (FCS) (3) (2013)
Perri, S.: Design of flexible hardware accelerators for image convolutions and transposed convolutions. J. Imaging 7, 210 (2021)
Prabakaran, N., Dudi, S.V., Palaniappan, R., Kannadasan, R., Sasidhar, V.: Forecasting the momentum using customised loss function for financial series. Int. J. Intell. Comput. Cybern. 14(4), 702–713 (2021)
Pu, Y., Zhe, G., Henao, R., Xin, Y., Carin, L.: Variational autoencoder for deep learning of images, labels and captions. In: NIPS 2016 (2016)
Seo, J.H., Kim, Y.H.: Machine-learning approach to optimize smote ratio in class imbalance dataset for intrusion detection. Hindawi Limited (2018)
Shon, T., Moon, J.: A hybrid machine learning approach to network anomaly detection. Inf. Sci. 177(18), 3799–3821 (2007)
Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar, A., Kang, B. (eds.) Australasian Joint Conference on Artificial Intelligence. LNCS, vol. 4304, pp. 1015–1021. Springer, Heidelberg (2006). https://doi.org/10.1007/11941439_114
Song, Y., Peng, Y.: A MCDM-based evaluation approach for imbalanced classification methods in financial risk prediction. IEEE Access 7, 84897–84906 (2019)
Wang, L.J., Jiang, Y.: Collocating recommendation method for E-commerce based on fuzzy C-means clustering algorithm. J. Math. 2022 (2022)
Yang, Y., Xu, Z.: Rethinking the value of labels for improving class-imbalanced learning (2020)
Zhao, Y., et al.: Constructing non-small cell lung cancer survival prediction model based on borderline-smote and PFS. Int. J. Biomed. Eng. 336–341 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, G. et al. (2024). D-AE: A Discriminant Encode-Decode Nets for Data Generation. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-54528-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-54527-6
Online ISBN: 978-3-031-54528-3
eBook Packages: Computer ScienceComputer Science (R0)