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Fast and accurate text classification via multiple linear discriminant projections

DSpace at IIT Bombay

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Title Fast and accurate text classification via multiple linear discriminant projections
 
Creator CHAKRABARTI, SOUMEN
ROY, SHOURYA
SOUNDALGEKAR, MAHESH V
 
Subject bayes methods
cache storage
data mining
decision trees
information retrieval
pattern recognition
 
Description Support vector machines (SVMs) have shown superb performance for text classification tasks. They are accurate, robust, and quick to apply to test instances. Their only potential drawback is their training time and memory requirement. For n training instances held in memory, the best-known SVM implementations take time proportional to na, where a is typically between 1.8 and 2.1. SVMs have been trained on data sets with several thousand instances, but Web directories today contain millions of instances that are valuable for mapping billions of Web pages into Yahoo!-like directories. We present SIMPL, a nearly linear-time classification algorithm that mimics the strengths of SVMs while avoiding the training bottleneck. It uses Fisher's linear discriminant, a classical tool from statistical pattern recognition, to project training instances to a carefully selected low-dimensional subspace before inducing a decision tree on the projected instances. SIMPL uses efficient sequential scans and sorts and is comparable in speed and memory scalability to widely used naive Bayes (NB) classifiers, but it beats NB accuracy decisively. It not only approaches and sometimes exceeds SVM accuracy, but also beats the running time of a popular SVM implementation by orders of magnitude. While describing SIMPL, we make a detailed experimental comparison of SVM-generated discriminants with Fisher's discriminants, and we also report on an analysis of the cache performance of a popular SVM implementation. Our analysis shows that SIMPL has the potential to be the method of choice for practitioners who want the accuracy of SVMs and the simplicity and speed of naive Bayes classifiers.
 
Publisher Springer
 
Date 2009-11-26T05:07:48Z
2011-11-25T15:54:44Z
2011-12-26T13:05:26Z
2011-12-27T05:52:21Z
2009-11-26T05:07:48Z
2011-11-25T15:54:44Z
2011-12-26T13:05:26Z
2011-12-27T05:52:21Z
2003
 
Type Article
 
Identifier The VLDB Journal 12(2), 170-185
0949-877X
http://dx.doi.org/10.1007/s00778-003-0098-9
http://hdl.handle.net/10054/1723
http://dspace.library.iitb.ac.in/xmlui/handle/10054/1723
 
Language en