• Corpus ID: 146808033

Improving classification performance using genetic programming to evolve string kernels

@article{Sultan2019ImprovingCP,
  title={Improving classification performance using genetic programming to evolve string kernels},
  author={Ruba Sultan and Hashem Tamimi and Yaqoub Ashhab},
  journal={Int. Arab J. Inf. Technol.},
  year={2019},
  volume={16},
  pages={454-459}
}
The objective of this work is to present a novel evolutionary-based approach that can create and optimize powerful string kernels using Genetic Programming. The proposed model creates and optimizes a superior kernel, which is expressed as a combination of string kernels, their parameters, and corresponding weights. As a proof of concept to demonstrate the feasibility of the presented approach, classification performance of the newly evolved kernel versus a group of conventional single string… 
1 Citations

Figures and Tables from this paper

References

SHOWING 1-10 OF 23 REFERENCES

The Genetic Kernel Support Vector Machine: Description and Evaluation

This paper proposes a classification technique, which it is called the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier.

Large Scale Multiple Kernel Learning

It is shown that the proposed multiple kernel learning algorithm can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations, and generalize the formulation and the method to a larger class of problems, including regression and one-class classification.

A hybrid approach for gene selection and classification using support vector machine

An ensemble feature selection technique which is a combination of Recursive Feature Elimination (RFE) and Based Bayes error Filter (BBF) for gene selection and Support Vector Machine (SVM) algorithm for classification is proposed.

Support vector machines for classification and regression.

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described, including its application to multivariate calibration, and why it is useful when there are outliers and non-linearities.

Kernel Methods in Computational Biology

This book presents different ideas for the design of kernel functions specifically adapted to various biological data, and covers different approaches to learning from heterogeneous data.

Multiple Kernel Learning Algorithms

Overall, using multiple kernels instead of a single one is useful and it is believed that combining kernels in a nonlinear or data-dependent way seems more promising than linear combination in fusing information provided by simple linear kernels, whereas linear methods are more reasonable when combining complex Gaussian kernels.

GPLAB A Genetic Programming Toolbox for MATLAB

This paper presents GPLAB, a genetic programming toolbox for MATLAB that implements most of the features traditionally used in genetic programming, as well as a modified version of a previously published method for automatically adapting the genetic operator probabilities in runtime, which makes it possible to use the toolbox as a test bench for new genetic operators.

The SHOGUN Machine Learning Toolbox

A machine learning toolbox designed for unified large-scale learning for a broad range of feature types and learning settings, which offers a considerable number of machine learning models such as support vector machines, hidden Markov models, multiple kernel learning, linear discriminant analysis, and more.

Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools

This article extensively reviews existing pan- specific methods and their web servers and presents a general framework of pan-specific methods, and discusses the future direction to improve pan- Specific methods for MHC-peptide binding prediction.

Kernel Methods for Pattern Analysis

This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.