BREAST CANCER DIAGNOSIS AND PROGNOSIS USING DIFFERENT KERNEL-BASED CLASSIFIERS

Tingting Mu, Asoke K. Nandi

2008

Abstract

The medical applications of several advanced, kernel-based classifiers to breast cancer diagnosis and prognosis are studied and compared in this paper, including kernel Fisher’s discriminative analysis, support vector machines (SVMs), multisurface proximal SVMs, as well as the pairwise Rayleigh quotient classifier and the strict 2-surface proximal classifier that we recently proposed. The radial basis function kernel is employed to incorporate nonlinearity. Studies are conducted with the Wisconsin diagnosis and prognosis breast cancer datasets generated from fine-needle-aspiration samples by image processing. Comparative analysis is provided in terms of classification accuracy, computing time, and sensitivity to the regularization parameters for the above classifiers.

Paper Citation

in Harvard Style

Mu T. and K. Nandi A. (2008). BREAST CANCER DIAGNOSIS AND PROGNOSIS USING DIFFERENT KERNEL-BASED CLASSIFIERS . In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 342-348. DOI: 10.5220/0001059303420348

in Bibtex Style

@conference{biosignals08,
author={Tingting Mu and Asoke K. Nandi},
title={BREAST CANCER DIAGNOSIS AND PROGNOSIS USING DIFFERENT KERNEL-BASED CLASSIFIERS},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={342-348},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001059303420348},
isbn={978-989-8111-18-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008)
TI - BREAST CANCER DIAGNOSIS AND PROGNOSIS USING DIFFERENT KERNEL-BASED CLASSIFIERS
SN - 978-989-8111-18-0
AU - Mu T.
AU - K. Nandi A.
PY - 2008
SP - 342
EP - 348
DO - 10.5220/0001059303420348