Resumo:
Exoplanets are planets discovered outside our solar system. Their discovery happens because
of scientific work with telescopes such as the Kepler. The data collected by Kepler
is known as Kepler Object of Interest. Machine Learning algorithms are trained to classify
these data into exoplanets or non-exoplanets. An Ensemble Algorithm is a type of
Machine Learning technique that combines the prediction performance of two or more
algorithms to gain an improved final prediction. The current works on exoplanet identification
use mostly traditional non-Ensemble algorithms. Therefore, research that uses
Ensemble algorithms for exoplanet identification is scarce. This paper performs a comparison
among some Ensemble algorithms on the exoplanet identification process. Each
algorithm is implemented with a set of different values for its parameters and executed
multiple times. All executions are performed with the cross-validation method. A confusion
matrix is created for each algorithm implementation. The results of each confusion
matrix provided data to evaluate the following algorithm’s performance metrics: accuracy,
sensitivity, specificity, precision, and F1 score. The Ensemble algorithms achieved
an average performance of more than 80% in all metrics. Changing the default values
of the Ensemble algorithms parameters improved their predictive performance. The algorithm
with the best performance is Stacking. In summary, the Ensemble algorithms
have great potential to improve exoplanet prediction. The Stacking algorithm achieved a
higher performance than the other algorithms. This aspect is discussed in the text. The
results of this work show that it is reasonable to increase the use of Ensemble algorithms.
The reason is their high prediction performance to improve exoplanet identification.
