Classifying large data sets using SVMs with hierarchical clusters

By Hwanjo Yu, Jiong Yang, Jiawei Han. Knowledge Discovery and Data Mining, 2003

353 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0752ae56f80af6f69ddab7db2b46161f44e03ebf

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Citations in the corpus

Recent Advances of Large-Scale Linear Classification (2012, methodology): “For example, the approach in [109] selects a subset by reading data from disk only once.”
Detecting Management Fraud in Public Companies (2010, neutral): “SVMs also scale up to very large data sets (Yu et al. 2003) and have been applied to problems involving text data (Joachims 1998), pictures (Shawe-Taylor and Cristianini 2004), etc.”
A new intrusion detection system using support vector machines and hierarchical clustering (2007, methodology): “However, such approaches are still not feasible with large data sets where even multiple scans of entire data set are too expensive to perform, or result in the loss through over-simplification of any benefit to be gained through the use of SVM [41].”
Core Vector Machines: Fast SVM Training on Very Large Data Sets (2005, methodology): “Yu et al. (2003) used the clustering-based SVM (CB-SVM), which incorporates the hierarchical micro-clustering algorithm BIRCH (Zhang et al., 1996) to reduce the number of patterns in SVM training.”