A class possibility based kernel to increase classification accuracy for small data sets using support vector machines

Authors:
Der-Chiang Li;Chiao-Wen Liu
Affiliations:
Department of Industrial and Information Management, National Cheng Kung University, 1, University Road, Tainan 70101, Taiwan, ROC;Department of Industrial and Information Management, National Cheng Kung University, 1, University Road, Tainan 70101, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 11
Cited 3

Support-Vector Networks

Machine Learning
Improving support vector machine classifiers by modifying kernal functions

Neural Networks
Conformal Transformation of Kernel Functions: A Data-Dependent Way to Improve Support Vector Machine Classifiers

Neural Processing Letters
Linear Programming and Network Flows

Linear Programming and Network Flows
Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation
On a theory of learning with similarity functions

ICML '06 Proceedings of the 23rd international conference on Machine learning
A new method to help diagnose cancers for small sample size

Expert Systems with Applications: An International Journal
A survey of kernel and spectral methods for clustering

Pattern Recognition
How good is a kernel when used as a similarity measure?

COLT'07 Proceedings of the 20th annual conference on Learning theory
Effects of kernel function on Nu support vector machines in extreme cases

IEEE Transactions on Neural Networks
Global Convergence of Decomposition Learning Methods for Support Vector Machines

IEEE Transactions on Neural Networks

Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping

Expert Systems with Applications: An International Journal
Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery

Computers & Geosciences
An adaptive support vector regression based on a new sequence of unified orthogonal polynomials

Pattern Recognition

Quantified Score

Hi-index	12.05

Visualization

Abstract

Appropriate choice of kernels is the most important task when using kernel-based learning methods such as support vector machines. The current widely used kernels (such as polynomial kernel, Gaussian kernel, two-layer perceptron kernel, and so on) are all functional kernels for general purposes. Currently, there is no kernel proposed in a data-driven way. This paper proposes a new kernel generating method dependent on classifying related properties of the data structure itself. The new kernel concentrates on the similarity of paired data in classes, where the calculation of similarity is based on fuzzy theories. The experimental results with four medical data sets show that the proposed kernel has superior classification performance than polynomial and Gaussian kernels.