Title Determination of relative agrarian technical efficiency by a dynamic over-sampling procedure guided by minimum sensitivity
Authors FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Hervás-Martínez C., GARCÍA ALONSO, CARLOS, TORRES JIMÉNEZ, MERCEDES, GARCÍA ALONSO, CARLOS, TORRES JIMÉNEZ, MERCEDES, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS
External publication No
Means Expert Sys Appl
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 2.20300
SJR Impact 1.11300
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-79958018912&doi=10.1016%2fj.eswa.2011.04.031&partnerID=40&md5=6bbab4249b1300fe86e0df6f4b5122ba
Publication date 01/01/2011
ISI 000292169500053
Scopus Id 2-s2.0-79958018912
DOI 10.1016/j.eswa.2011.04.031
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%). © 2010 Elsevier Ltd. All rights reserved.
Keywords Accuracy; APS; DEA-Montecarlo; Hybrid algorithm; Imbalanced Data-sets; Multi-classification; Over sampling; Sensitivity; SMOTE; Algorithms; Data envelopment analysis; Digital signal processing; Effici
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