Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision

Abstract: Genetic algorithms (GAs) are efficient search methods based on theparadigm of natural selection and population genetics. A simple GA was appliedfor selecting the optimal feature subset among an initial feature set of larger size.The performances were tested on a practical pattern recognition problem, whichconsisted on the discrimination between four seed species (two cultivated andtwo adventitious seed species) by artiÐcial vision. A set of 73 features, describingsize, shape and texture, were extracted from colour images in order to character-ise each seed. The goal of the GA was to select the best subset of features whichgave the highest classiÐcation rates when using the nearest neighbour as a classi-Ðcation method. The selected features were represented by binary chromosomeswhich had 73 elements. The number of selected features was directly related tothe probability of initialisation of the population at the Ðrst generation of theGA. When this probability was Ðxed to 0E1, the GA selected about Ðve features.The classiÐcation performances increased with the number of generations. Forexample, 6E25% of the seeds were misclassiÐed by using Ðve features at gener-ation 140, whereas another subset of the same size led to 3% misclassiÐcation atgeneration 400. The present work shows the great potential of GAs for featureselection (dimensionality reduction) problems. 1998 SCI.(J Sci Food Agric 76,77E86 (1998)Key words: feature selection; genetic algorithm; seed; colour image analysis;classiÐcation; discrimination

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