Classification of chromosome sequences with entropy kernel and LKPLS algorithm

Zhenqiu Liu; Dechang Chen

doi:10.1007/11538059_57

Classification of chromosome sequences with entropy kernel and LKPLS algorithm

Zhenqiu Liu^*, Dechang Chen

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

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 language	English
Pages (from-to)	543-551
Number of pages	9
Journal	Lecture Notes in Computer Science
Volume	3644
Issue number	PART I
DOIs	https://doi.org/10.1007/11538059_57
State	Published - 2005
Externally published	Yes
Event	International Conference on Intelligent Computing, ICIC 2005 - Hefei, China Duration: 23 Aug 2005 → 26 Aug 2005

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title = "Classification of chromosome sequences with entropy kernel and LKPLS algorithm",

abstract = "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.",

author = "Zhenqiu Liu and Dechang Chen",

note = "Funding Information: D. Chen was supported by the National Science Foundation grant CCR-0311252. The authors wish to thank Dr. Jens Gregor for providing the preprocessed chromosome data. Note: The opinions expressed herein are those of the authors and do not necessarily represent those of the Uniformed Services University of the Health Sciences and the Department of Defense.; International Conference on Intelligent Computing, ICIC 2005 ; Conference date: 23-08-2005 Through 26-08-2005",

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T1 - Classification of chromosome sequences with entropy kernel and LKPLS algorithm

AU - Liu, Zhenqiu

AU - Chen, Dechang

N1 - Funding Information: D. Chen was supported by the National Science Foundation grant CCR-0311252. The authors wish to thank Dr. Jens Gregor for providing the preprocessed chromosome data. Note: The opinions expressed herein are those of the authors and do not necessarily represent those of the Uniformed Services University of the Health Sciences and the Department of Defense.

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

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U2 - 10.1007/11538059_57

DO - 10.1007/11538059_57

M3 - Conference article

AN - SCOPUS:27144550810

SN - 0302-9743

VL - 3644

SP - 543

EP - 551

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

IS - PART I

T2 - International Conference on Intelligent Computing, ICIC 2005

Y2 - 23 August 2005 through 26 August 2005

ER -

Classification of chromosome sequences with entropy kernel and LKPLS algorithm

Abstract

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