The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Dealing with Noise Problem in Machine Learning Data-sets: A Systematic Review
Open accessAbstract
The occurrences of noisy data in data set can significantly impact prediction of any meaningful information. Many empirical studies have shown that noise in data set dramatically led to decreased classification accuracy and poor prediction results. Therefore, the problem of identifying and handling noise in prediction application has drawn considerable attention over past many years. In our study, we performed a systematic literature review of noise identification and handling studies published in various conferences and journals between January 1993 to July 2018. We have identified 79 primary studies are of noise identification and noise handling techniques. After investigating these studies, we found that among the noise identification schemes, the accuracy of identification of noisy instances by using ensemble-based techniques are better than other techniques. But regarding efficiency, usually single based techniques method is better; it is more suitable for noisy data sets. Among noise handling techniques, polishing techniques generally improve classification accuracy than filtering and robust techniques, but it introduced some errors in the data sets.
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Cites
Class Noise vs. Attribute Noise: A Quantitative Study
Zhu, X., & Wu, X. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-0751-8 · Cited by 893
Dealing with noise in defect prediction
Kim, S., Zhang, H., Wu, R., & Gong, L. (2011) · Proceedings of the 33rd International Conference on Software Engineering · doi:10.1145/1985793.1985859 · Cited by 330
Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data
Khoshgoftaar, T. M., Van Hulse, J., & Napolitano, A. (2011) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2010.2084081 · Cited by 300
Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais-Sicre, C., & Dedieu, G. (2017) · Remote Sensing · doi:10.3390/rs9020173 · Cited by 242
Repeated labeling using multiple noisy labelers
Ipeirotis, P. G., Provost, F., Sheng, V. S., & Wang, J. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-013-0306-1 · Cited by 181
Ensemble Methods for Noise Elimination in Classification Problems
Verbaeten, S., & Van Assche, A. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-44938-8_32 · Cited by 168
Effect of label noise in the complexity of classification problems
Garcia, L. P. F., de Carvalho, A. C. P. L. F., & Lorena, A. C. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.10.085 · Cited by 148
A noise-detection based AdaBoost algorithm for mislabeled data
Cao, J., Kwong, S., & Wang, R. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.05.002 · Cited by 147
Identifying and Handling Mislabelled Instances
Muhlenbach, F., Lallich, S., & Zighed, A. D. (2003) · Journal of Intelligent Information Systems · doi:10.1023/a:1025832930864 · Cited by 142
Noise detection and elimination in data preprocessing: Experiments in medical domains
Gamberger, D., Nada Lavrač, A., & Džeroski, S. (2000) · Applied Artificial Intelligence · doi:10.1080/088395100117124 · Cited by 135
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Maharana, K., Mondal, S., & Nemade, B. (2022) · Global Transitions Proceedings · doi:10.1016/j.gltp.2022.04.020 · Cited by 1,337
Young, R. A., Martin, C. M., Sturmberg, J. P., Hall, S., Bazemore, A., Kakadiaris, I. A., & Lin, S. (2024) · The Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2023.230219r1 · Cited by 17
Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review
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Metadata
- Authors
- Gupta, S., & Gupta, A.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2019
- Volume
- 161
- Pages
- 466-474
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2997655715
- Cited by
- 325
- References
- 42
- FWCI
- 7.71
- Type
- conference paper
- Language
- en
- Open access
- diamond
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