An ensemble of dissimilarity based classifiers for Mackerel gender determination

2nd International Conference on Mathematical Modeling in Physical Sciences 2013

物理学;数学

Blanco, A.^1 ; Rodriguez, R.^1 ; Martinez-Maranon, I.^1

AZTI-Tecnalia Astondo Bidea, Edificio 609, Parque Tecnológico de Bizkaia, 48160 Derio Bizkaia, Spain^1

关键词: Colour measurement;    Euclidean distance;    K-nearest neighbors;    Linear discriminant analysis;    Non-Euclidean;    Nonlinear classifiers;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/490/1/012130/pdf
DOI  :  10.1088/1742-6596/490/1/012130

来源: IOP

【 摘 要 】

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.