The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Classifying large data sets using SVMs with hierarchical clusters
Abstract
Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convery several salient properties that other methods hardly provide. However, despite the prominent properties of SVMs, they are not as favored for large-scale data mining as for pattern recognition or machine learning because the training complexity of SVMs is highly dependent on the size of a data set. Many real-world data mining applications involve millions or billions of data records where even multiple scans of the entire data are too expensive to perform. This paper presents a new method, Clustering-Based SVM (CB-SVM), which is specifically designed for handling very large data sets. CB-SVM applies a hierarchical micro-clustering algorithm that scans the entire data set only once to provide an SVM with high quality samples that carry the statistical summaries of the data such that the summaries maximize the benefit of learning the SVM. CB-SVM tries to generate the best SVM boundary for very large data sets given limited amount of resources. Our experiments on synthetic and real data sets show that CB-SVM is highly scalable for very large data sets while also generating high classification accuracy.
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Cites
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Cited by
A comprehensive survey on support vector machine classification: Applications, challenges and trends
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.118 · Cited by 2,381
A new intrusion detection system using support vector machines and hierarchical clustering
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Detecting Management Fraud in Public Companies
Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010) · Management Science · doi:10.1287/mnsc.1100.1174 · Cited by 367
Large linear classification when data cannot fit in memory
Yu, H.-F., Hsieh, C.-J., Chang, K.-W., & Lin, C.-J. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2086737.2086743 · Cited by 69
Self-citationA Survey of Classification Techniques in the Area of Big Data
Koturwar, P., Girase, S., & Mukhopadhyay, D. (2015) · arXiv · doi:10.48550/arxiv.1503.07477 · Cited by 35
Metadata
- Authors
- Yu, H., Yang, J., & Han, J.
- Venue
- Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2003-08-24
- Pages
- 306-315
- OpenAlex
- W1965059296
- Cited by
- 273
- References
- 26
- FWCI
- 8.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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