A tutorial on support vector machine-based methods for classification problems in chemometrics

This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approach...

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Bibliographic Details
Published in:Analytica chimica acta 2010-04, Vol.665 (2), p.129-145
Main Authors: Luts, Jan, Ojeda, Fabian, Van de Plas, Raf, De Moor, Bart, Van Huffel, Sabine, Suykens, Johan A.K.
Format: Article
Language:English
Subjects:
Algorithms
Analytical chemistry
Artificial Intelligence
Biological and medical sciences
Brain Neoplasms - classification
Chemistry
Classification
Diverse techniques
Exact sciences and technology
Feature selection
Fundamental and applied biological sciences. Psychology
Genetics
Image Interpretation, Computer-Assisted
Kernel logistic regression
Kernel-based learning
Least squares method
Least squares support vector machine
Least-Squares Analysis
Logistic Models
Mathematical analysis
Mathematical models
Molecular and cellular biology
Multi-class probabilities
Pattern Recognition, Automated - methods
Regression
Support vector machine
Support vector machines
Tuning
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Summary:This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approaches to retrieve a probabilistic interpretation are covered and it is explained how the binary classification techniques can be extended to multi-class methods. Kernel logistic regression, which is closely related to iteratively weighted least squares support vector machines, is discussed. Different practical aspects of these methods are addressed: the issue of feature selection, parameter tuning, unbalanced data sets, model evaluation and statistical comparison. The different concepts are illustrated on three real-life applications in the field of metabolomics, genetics and proteomics.
ISSN:0003-2670
1873-4324
DOI:10.1016/j.aca.2010.03.030