Texture Classification Using Kernel-Based Techniques

Seoane

Gestal

et al. 2016

Sci Rep

Self Cite

Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several different kernel-based machine learning techniques to classify proteins in 2-DE images into spot and noise. We evaluate the classification accuracy of each of these techniques with proteins extracted from ten 2-DE images of different types of tissues and different experimental conditions. We found that the best classification model was FSMKL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% while using a reduced number of features. This technique allows us to increment the interpretability of the complex combinations of textures and to weight the importance of each particular feature in the final model. In particular the Inverse Difference Moment exhibited the highest discriminating power. A higher value can be associated with an homogeneous structure as this feature describes the homogeneity; the larger the value, the more symmetric. The final model is performed by the combination of different groups of textural features. Here we demonstrated the feasibility of combining different groups of textures in 2-DE image analysis for spot detection.

“…These images are from the dataset owned by G.-Z. Yang 44 (Imperial College of Science, Technology and Medicine, London) and have been used in several publications 2 45 46 .…”

Section: Methodsmentioning

confidence: 99%

Texture analysis in gel electrophoresis images using an integrative kernel-based approach

Seoane

Gestal

et al. 2016

Sci Rep

Self Cite

“…GAs for feature selection were first proposed by Siedlecki and Sklansky [ 52 ]. Many studies have been done on GA for feature selection since then [ 6 , 53 ], concluding that GA is suitable for finding optimal solutions to large problems with more than 40 features to select from. GA for feature selection could be used in combination with a classifier such SVM, optimizing it.…”

Section: Methodsmentioning

confidence: 99%

“…There have been several studies that have opted for the combination of evolutionary methods with classification mechanisms [ 6 , 13 , 15 – 17 ]. However, even after having developed very efficient models, ANNs have the disadvantage of undergoing training processes on a strong stochastic basis, leading to nonrepeatability of the process.…”

Section: State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

The Scientific World Journal

Canto

Gestal

et al. 2013

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.

“…The hyperplane separates the "positive" from the "negative" examples, so that the distance between the boundary and the nearest data point in each class is maximal; these data points are used to define the margins, and are called support vectors (Burges, 1998). SVMs have proved to be exceptionally efficient in classification problems of higher dimensionality (Moulin et al, 2004;Chapelle et al, 1999;Fernandez-Lozano et al, 2013a), because of their ability to generalize in high-dimensional spaces. SVM uses different non-linear kernel functions, like polynomial, sigmoid and radial basis function, where the non-linear SVM maps the training samples from the input spaces into a higher-dimensional feature space via a mapping function (Burges, 1998).…”

Section: Svmmentioning

confidence: 99%

Using genetic algorithms to improve support vector regression in the analysis of atomic spectra of lubricant oils

Engineering Computations

Cedrón

Rivero

et al. 2016

Self Cite

Purpose.-The purpose of this paper is to assess the quality of commercial lubricant oils. A spectroscopic method was used in combination with multivariate regression techniques (ordinary multivariate multiple regression, principal components analysis, partial least squares, and support vector regression (SVR)). Design/methodology/approach.-The rationale behind the use of SVR was the fuzzy characteristics of the signal and its inherent ability to find nonlinear, global solutions in highly complex dimensional input spaces. Thus, SVR allows extracting useful information from calibration samples that makes it possible to characterize physical-chemical properties of the lubricant oils. Findings.-A dataset of 42 spectra measured from oil standards was studied to assess the concentration of copper into the oils and, thus, evaluate the wearing of the machinery. It was found that the use of SVR was very advantageous to get a regression model. Originality/value.-The use of genetic algorithms coupled to SVR was considered in order to reduce the time needed to find the optimal parameters required to get a suitable prediction model.