Abstract
An ensemble of classifiers, or a systematic combination of individual classifiers, often results in better classifications in comparison to a single classifier. However, the question regarding what classifiers should be chosen for a given situation to construct an optimal ensemble has often been debated. In addition, ensembles are often computationally expensive since they require the execution of multiple classifiers for a single classification task. To address these problems, we propose a hybrid approach for selecting and combining data mining models to construct ensembles by integrating Data Envelopment Analysis and stacking. Experimental results show the efficiency and effectiveness of the proposed approach.
Highlights
Coffee production is the main agricultural activity in Colombia
This section describes the data collection process and the generation of datasets used in experiments, and introduces three classifiers for prediction: Support Vector Regression, Backpropagation Neural Network, and Regression Tree M5
A) Performance Evaluation Methods Pearson correlation coefficient (PCC) In statistics, Pearson correlation coefficient is a measure of how well a linear equation describes the relation between two variables and measured on the same object or organism
Summary
Coffee production is the main agricultural activity in Colombia. More than 50 percent of the country’s coffee crop is still susceptible in the productive phase. Studies on coffee rust have concluded that the spores carrying the infection are spread by climatic elements such as wind and rainfall (Becker, 1979). Once spores make contact with a susceptible leaf, the infection process is increased by high shadow index, high humidity (atmosphere and leaf), soil acidity, high coffee tree density and low soil fertility. The dataset proposed joins each of the favorable conditions that coffee rust requires to infect the crop, by taking prophylactic measures (biological, chemical and weather), in order to allow the prevention of the onset of the disease
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