Abstract: This paper proposes a novel hybrid algorithm for feature selection based on a binary ant colony and SVM. The final subset selection is attained through the elimination of the features that produce noise or, are strictly correlated with other already selected features. Our algorithm can improve classification accuracy with a small and appropriate feature subset. Proposed algorithm is easily implemented and because of use of a simple filter in that, its computational complexity is very low. The performance of the proposed algorithm is evaluated through a real Rotary Cement kiln dataset. The results show that our algorithm outperforms existing algorithms.
Abstract: Among neural models the Support Vector Machine
(SVM) solutions are attracting increasing attention, mostly because
they eliminate certain crucial questions involved by neural network
construction. The main drawback of standard SVM is its high
computational complexity, therefore recently a new technique, the
Least Squares SVM (LS–SVM) has been introduced. In this paper we
present an extended view of the Least Squares Support Vector
Regression (LS–SVR), which enables us to develop new
formulations and algorithms to this regression technique. Based on
manipulating the linear equation set -which embodies all information
about the regression in the learning process- some new methods are
introduced to simplify the formulations, speed up the calculations
and/or provide better results.
Abstract: The amount of the information being churned out by the field of biology has jumped manifold and now requires the extensive use of computer techniques for the management of this information. The predominance of biological information such as protein sequence similarity in the biological information sea is key information for detecting protein evolutionary relationship. Protein sequence similarity typically implies homology, which in turn may imply structural and functional similarities. In this work, we propose, a learning method for detecting remote protein homology. The proposed method uses a transformation that converts protein sequence into fixed-dimensional representative feature vectors. Each feature vector records the sensitivity of a protein sequence to a set of amino acids substrings generated from the protein sequences of interest. These features are then used in conjunction with support vector machines for the detection of the protein remote homology. The proposed method is tested and evaluated on two different benchmark protein datasets and it-s able to deliver improvements over most of the existing homology detection methods.