Probabilistic Adaptive Crossover Applied to Chilean Wine Classification

Recently, a new crossover technique for genetic algorithms has been proposed. The technique, called probabilistic adaptive crossover (PAX), includes the estimation of the probability distribution of the population, storing the information regarding the best and the worst solutions of the problem being solved in a probability vector. The use of the proposed technique to face Chilean wine classification based on chromatograms obtained from an HPLC is reported in this paper. PAX is used in the first stage as the feature selection method and then support vector machines (SVM) and linear discriminant analysis (LDA) are used as classifiers. The results are compared with those obtained using the uniform (discrete) crossover standard technique and a variant of PAX called mixed crossover.

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A Texture-based Classification Method for Proteins in Two-Dimensional Electrophoresis Gel Images - A Feature Selection Method using Support Vector Machines and Genetic Algorithms

Proceedings of the International Conference on Computer Vision Theory and Applications ◽

10.5220/0004208704010404 ◽

2013 ◽

Keyword(s):

Genetic Algorithms ◽

Feature Selection ◽

Support Vector Machines ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Two Dimensional ◽

Two Dimensional Electrophoresis ◽

Vector Machines ◽

Dimensional Electrophoresis

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FS/spl I.bar/SFS: a novel feature selection method for support vector machines

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing ◽

10.1109/icassp.2004.1327231 ◽

2004 ◽

Author(s):

Yi Liu ◽

Y.F. Zheng

Keyword(s):

Feature Selection ◽

Support Vector Machines ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Vector Machines

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An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default

Review of Financial Economics ◽

10.1002/rfe.1049 ◽

2018 ◽

Vol 37 (3) ◽

pp. 404-427 ◽

Cited By ~ 1

Author(s):

Eduard Sariev ◽

Guido Germano

Keyword(s):

Feature Selection ◽

Support Vector Machines ◽

Credit Risk ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Vector Machines

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SVM-Based Credit Rating and Feature Selection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.618.573 ◽

2014 ◽

Vol 618 ◽

pp. 573-577 ◽

Cited By ~ 1

Author(s):

Yu Qiang Qin ◽

Yu Dong Qi ◽

Hui Ying

Keyword(s):

Logistic Regression ◽

Feature Selection ◽

Financial Institutions ◽

Credit Card ◽

Credit Rating ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Vector Machines ◽

Reference Agency

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit rating for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines (SVM) against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default.

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