Kyaw Kyaw Htike, “Efficient determination of the number of weak learners in AdaBoost”, Journal of Experimental and Theoretical Artificial Intelligence, Vol. 29, no. 5, pp. 967-982, Taylor & Francis, 2017. DOI: 10.1080/0952813X.2016.1266038. [ISI and Scopus-indexed journal; Impact factor = 1.703]
AdaBoost is a successful machine learning algorithm used in a variety of fields nowadays. However, its performance is sensitive to the number of weak learners in the ensemble. Too few weak learners will result in underfitting to the training data-set and too many of them cause overfitting to the training data-set, both of which result in poor generalisation of the classifier on test data. The standard way to compute the number of weak learners that is optimal for a particular data-set is to use cross-validation; however, it is highly computationally expensive. In this paper, we propose an efficient method that does not require cross-validation or a separate validation set to determine the number of weak learners for use in AdaBoost. Our method is evaluated on eight different publicly available data-sets to demonstrate its efficacy.