Determination of relative agrarian technical efficiency by a dynamic over-sampling procedure guided by minimum sensitivity

Authors:
Francisco Fernández-Navarro;César Hervás-Martínez;C. García-Alonso;M. Torres-Jimenez
Affiliations:
Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd Floor, 14071 Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd Floor, 14071 Córdoba, Spain;Department of Management and Quantitative Methods, ETEA, Escritor Castilla Aguayo 4, 14004 Córdoba, Spain;Department of Management and Quantitative Methods, ETEA, Escritor Castilla Aguayo 4, 14004 Córdoba, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this paper, a dynamic over-sampling procedure is proposed to improve the classification of imbalanced datasets with more than two classes. This procedure is incorporated into a Hybrid algorithm (HA) that optimizes Multi Layer Perceptron Neural Networks (MLPs). To handle class imbalance, the training dataset is resampled in two stages. In the first stage, an over-sampling procedure is applied to the minority class to partially balance the size of the classes. In the second, the HA is run and the dataset is over-sampled in different generations of the evolution, generating new patterns in the minimum sensitivity class (the class with the worst accuracy for the best MLP of the population). To evaluate the efficiency of our technique, we pose a complex problem, the classification of 1617 real farms into three classes (efficient, intermediate and inefficient) according to the Relative Technical Efficiency (RTE) obtained by the Monte Carlo Data Envelopment Analysis (MC-DEA). The multi-classification model, named Dynamic Smote Hybrid Multi Layer Perceptron (DSHMLP) is compared to other standard classification methods with an over-sampling procedure in the preprocessing stage and to the threshold-moving method where the output threshold is moved toward inexpensive classes. The results show that our proposal is able to improve minimum sensitivity in the generalization set (35.00%) and obtains a high accuracy level (72.63%).