Machine Learning
Artificial neural network model for predicting HIV protease cleavage sites in protein
Advances in Engineering Software
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
An empirical comparison of supervised machine learning techniques in bioinformatics
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Algorithms for Splicing Junction Donor Recognition in Genomic DNA Sequences
INTSYS '98 Proceedings of the IEEE International Joint Symposia on Intelligence and Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Peptide programs: applying fragment programs to protein classification
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
Creating ensembles of classifiers via fuzzy clustering and deflection
Fuzzy Sets and Systems
Using fuzzy support vector machine network to predict low homology protein structural classes
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
Wavelet selection for disease classification by DNA microarray data
Expert Systems with Applications: An International Journal
Reduced Reward-punishment editing for building ensembles of classifiers
Expert Systems with Applications: An International Journal
A GA based approach to improving the ICA based classification models for tumor classification
WSEAS Transactions on Information Science and Applications
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This paper studies the problem of building a machine learning method for biological data. Various feature selection methods and classifier design strategies have been generally used and compared. However, most published articles have applied a certain technique to a certain dataset, and recently several researchers compared these techniques based on several public datasets. We propose an ensemble of classifiers that combine a linear classifier, linear support vector machine, a non-linear classifier, radial basis-support vector machines and a Subspace Classifier. We validate our new method on several recent publicly available datasets both with predictive accuracy of testing samples and through cross validation. Compared with the best performance of other current methods, remarkably improved results can be obtained using our new strategy on a wide range of different datasets. On a wide range of recently published datasets, our method performs better, or is at least comparable to, the current best methods of our knowledge.