Classification of chromosome sequences with entropy kernel and LKPLS algorithm

Zhenqiu Liu*, Dechang Chen

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


Kernel methods such as support vector machines have been used extensively for various classification tasks. In this paper, we describe an entropy based string kernel and a novel logistic kernel partial least square algorithm for classification of sequential data. Our experiments with a human chromosome dataset show that the new kernel can be computed efficiently and the algorithm leads to a high accuracy especially for the unbalanced training data.

Original languageEnglish
Pages (from-to)543-551
Number of pages9
JournalLecture Notes in Computer Science
Issue numberPART I
StatePublished - 2005
Externally publishedYes
EventInternational Conference on Intelligent Computing, ICIC 2005 - Hefei, China
Duration: 23 Aug 200526 Aug 2005


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