An effective support vector machines (SVMs) performance using hierarchical clustering

Mamoun Awad, Latifur Khan, Farokh Bastani, I. Ling Yen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Citations (Scopus)

Abstract

The training time for SVMs to compute the maximal marginal hyper-plane is at least O(N 2) with the data set size N, which makes it non-favorable for large data sets. This paper presents a study for enhancing the training time of SVMs, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the Dynamically Growing Self-Organizing Tree (DGSOT) Algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering algorithms. Clustering analysis helps find the boundary points, which are the most qualified data points to train SVMs, between two classes. We present a new approach of combination of SVMs and DGSOT, which starts with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We compare our approach with the Rocchio Bundling technique in terms of accuracy loss and training time gain using two benchmark real data sets.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004
EditorsT.M. Khoshgoftaar
Pages663-667
Number of pages5
DOIs
Publication statusPublished - Dec 1 2004
Externally publishedYes
EventProceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004 - Boca Raton, FL, United States
Duration: Nov 15 2004Nov 17 2004

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Other

OtherProceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004
Country/TerritoryUnited States
CityBoca Raton, FL
Period11/15/0411/17/04

ASJC Scopus subject areas

  • Engineering(all)

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