Scaling KNN multi-class twin support vector machine via safe instance reduction

Scaling KNN multi-class twin support vector machine via safe instance reduction k-nearest neighbor-based weighted multi-class twin support vector machine(KMTSVM) is an effective algorithm to deal with multi-class optimization problems. The superiority of KMTSVM is that it adopts a “1-versus-1-versus-rest” structure, and takes the distribution information of all the instances into consideration. However, it costs much time to handle large scale problems. Motivated by the sparse solution of KMTSVM, in this paper, we propose a safe instance reduction rule to improve its computational efficiency, termed as SIR-KMTSVM. The SIR-KMTSVM can delete a majority of redundant instances both for focused classes and remaining classes, then the operation speed can be accelerated greatly. And our instance reduction rule is safe in the sense that the reduced problem can derive an identical optimal solution as the original one. More importantly, we analysis that the different k-nearest neighbors will have different acceleration effect on our SIR-KMTSVM. Besides, a fast algorithm DCDM is introduced to handle relatively large scale problems more efficiently. Sequential versions of SIR-KMTSVM are further introduced to substantially accelerate the whole training process. Experimental results on an artificial dataset, seventeen benchmark datasets and a real dataset confirm the effectiveness of our proposed algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge-Based Systems Elsevier

Scaling KNN multi-class twin support vector machine via safe instance reduction

Loading next page...
 
/lp/elsevier/scaling-knn-multi-class-twin-support-vector-machine-via-safe-instance-clNiqhgXfj
Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier B.V.
ISSN
0950-7051
D.O.I.
10.1016/j.knosys.2018.02.018
Publisher site
See Article on Publisher Site

Abstract

k-nearest neighbor-based weighted multi-class twin support vector machine(KMTSVM) is an effective algorithm to deal with multi-class optimization problems. The superiority of KMTSVM is that it adopts a “1-versus-1-versus-rest” structure, and takes the distribution information of all the instances into consideration. However, it costs much time to handle large scale problems. Motivated by the sparse solution of KMTSVM, in this paper, we propose a safe instance reduction rule to improve its computational efficiency, termed as SIR-KMTSVM. The SIR-KMTSVM can delete a majority of redundant instances both for focused classes and remaining classes, then the operation speed can be accelerated greatly. And our instance reduction rule is safe in the sense that the reduced problem can derive an identical optimal solution as the original one. More importantly, we analysis that the different k-nearest neighbors will have different acceleration effect on our SIR-KMTSVM. Besides, a fast algorithm DCDM is introduced to handle relatively large scale problems more efficiently. Sequential versions of SIR-KMTSVM are further introduced to substantially accelerate the whole training process. Experimental results on an artificial dataset, seventeen benchmark datasets and a real dataset confirm the effectiveness of our proposed algorithm.

Journal

Knowledge-Based SystemsElsevier

Published: May 15, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off