Boosting-based ensemble learning with penalty profiles for automatic Thai unknown word recognition
Computers & Mathematics with Applications
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Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores Genetic Programming and Boosting technique to obtain an ensemble of regressors and proposes a new formula for the final hypothesis. This new formula is based on the correlation coefficient instead of the geometric median used by the boosting algorithm. To validate this method, experiments were performed, the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using a Boosting technique and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.