Abstract
Class imbalance problem occurs when the training dataset contains significantly fewer samples of one class in contrast to another class. Conventional extreme learning machine (ELM) gives the same importance to all the samples leading to the results, which favor the majority class. To solve this intrinsic deficiency, modifications of ELM have been developed such as weighted ELM (WELM) and WELM based on the overall distribution (ODW-ELM). Recently class-specific ELM (CS-ELM) was designed for class imbalance learning. It has been shown in this work that the derivation of the output weights, \(\varvec{\beta }\), is more efficient compared to class-specific cost regulation ELM (CCRELM) for handling the class imbalance problem. Motivated by CCRELM, X. Luo et al. have proposed the classifier ODW-ELM, which is also not efficient for imbalance learning. In this work, a novel class-specific ELM based on overall distribution (OD-CSELM) and the kernelized version of OD-CSELM (OD-CSKELM) is proposed to address the binary class imbalance problem more effectively. OD-CSELM and OD-CSKELM are motivated by CS-ELM. In addition, the computational complexity of OD-CSELM and OD-CSKELM is significantly lower than WELM and kernelized WELM, respectively. The proposed work is evaluated by using the benchmark real-world imbalanced datasets. The experimental results demonstrate that the proposed work gives good generalization performance in contrast to the rest of the classifiers for class imbalance learning.
Similar content being viewed by others
Data Availability
Enquiries about data availability should be directed to the authors.
References
Alcalá J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2010) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple Val Logic Soft Comput 17(2–3):255–287
Bhattacharya S, Maddikunta PKR, Hakak S, Khan W, Bashir A, Jolfaei A, Tariq U, Gadekallu TR (2020) Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset
Bradley AP (1997) The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Int Res 16(1):321–357
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Deng W, Zheng Q, Chen L (2009) Regularized extreme learning machine. In: IEEE symposium on computational intelligence and data mining pp 389–395
Fawcett T (2003) Roc graphs: notes and practical considerations for researchers. Tech. rep., HP Labs, Tech. Rep. HPL-2003-4
Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C Appl Rev 42(4):463–484
Haixiang G, Yijing L, Shang J, Mingyun G, Yuanyue H, Bing G (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220–239
He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284
He H, Ma Y (2013) Class imbalance learning methods for support vector machines. Wiley-IEEE Press, New York
Huang J, Ling CX (2005) Using auc and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17(3):299–310
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529
Iosifidis A, Gabbouj M (2015) On the kernel extreme learning machine speedup. Pattern Recogn Lett 68:205–210
Iosifidis A, Tefas A, Pitas I (2015) On the kernel extreme learning machine classifier. Pattern Recogn Lett 54:11–17
Janakiraman VM, Nguyen X, Sterniak J, Assanis D (2015) Identification of the dynamic operating envelope of hcci engines using class imbalance learning. IEEE Trans Neural Netw Learn Syst 26(1):98–112
Janakiraman VM, Nguyen X, Assanis D (2016) Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines. Neurocomputing 177:304–316
Krawczyk B, Galar M, Jele L, Herrera F (2016) Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. Appl Soft Comput 38(C):714–726
Li K, Kong X, Lu Z, Wenyin L, Yin J (2014) Boosting weighted ELM for imbalanced learning. Neurocomputing 128:15–21
Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/ml
Liu XY, Wu J, Zhou ZH (2009) Exploratory undersampling for class-imbalance learning. IEEE Trans Syst Man Cybern Part B Cybern 39(2):539–550
Luo X, Wu H, Wang Z, Wang J, Meng D (2021) A novel approach to large-scale dynamically weighted directed network representation. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2021.3132503
Luo X, Jiang C, Wang W, Xu Y, Wang JH, Zhao W (2018) User behavior prediction in social networks using weighted extreme learning machine with distribution optimization. Fut Gen Comput Syst
Mathew J, Pang CK, Luo M, Leong WH (2018) Classification of imbalanced data by oversampling in kernel space of support vector machines. IEEE Trans Neural Netw Learn Syst pp 1–12
Patel H, Rajput DS, Reddy GT, Iwendi C, Bashir AK, Jo O (2020) A review on classification of imbalanced data for wireless sensor networks. Int J Distrib Sensor Netw 16(4):1550147720916404
Raghuwanshi BS, Shukla S (2018) Class-specific extreme learning machine for handling binary class imbalance problem. Neural Netw 105:206–217
Raghuwanshi BS, Shukla S (2018) Class-specific kernelized extreme learning machine for binary class imbalance learning. Appl Soft Comput 73:1026–1038
Raghuwanshi BS, Shukla S (2018) Underbagging based reduced kernelized weighted extreme learning machine for class imbalance learning. Eng Appl Artif Intell 74:252–270
Raghuwanshi BS, Shukla S (2021) Classifying imbalanced data using smote based class-specific kernelized elm. Int J Mach Learn Cybern 12:1255–1280
Sarmanova A, Albayrak S (2013) Alleviating class imbalance problem in data mining. In: 2013 21st signal processing and communications applications conference (SIU), pp 1–4
Wang S, Yao X (2013) Using class imbalance learning for software defect prediction. IEEE Trans Reliab 62(2):434–443
Wei W, Li J, Cao L, Ou Y, Chen J (2013) Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web 16(4):449–475
Wu D, Luo X, Shang M, He Y, Wang G, Wu X (2022) A data-characteristic-aware latent factor model for web services qos prediction. IEEE Trans Knowl Data Eng 34(6):2525–2538. https://doi.org/10.1109/TKDE.2020.3014302
Xiao W, Zhang J, Li Y, Zhang S, Yang W (2017) Class-specific cost regulation extreme learning machine for imbalanced classification. Neurocomputing 261:70–82
Yang X, Song Q, Wang Y (2007) A weighted support vector machine for data classification. Int J Pattern Recognit Artif Intell 21(05):961–976
Yuan Y, He Q, Luo X, Shang M (2022) A multilayered-and-randomized latent factor model for high-dimensional and sparse matrices. IEEE Trans Big Data 8(3):784–794
Zakaryazad A, Duman E (2016) A profit-driven artificial neural network (ann) with applications to fraud detection and direct marketing. Neurocomputing 175:121–131
Zhang A, Yu H, Zhou S, Huan Z, Yang X (2022) Instance weighted smote by indirectly exploring the data distribution. Knowl-Based Syst 249(108):919
Zong W, Huang GB, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomput 101:229–242
Funding
The authors have not disclosed any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Informed Consent
Informed consent was obtained from all individual participants included in the study.
Human and Animal Rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Raghuwanshi, B.S. Class-specific extreme learning machine based on overall distribution for addressing binary imbalance problem. Soft Comput 27, 4609–4626 (2023). https://doi.org/10.1007/s00500-022-07705-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-07705-5