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Abstract.Bagging is an ensemble method proposed to improve the predictive performance of learning algorithms, being specially effective when applied to unstable predictors. It is based on the aggregation of a certain number of prediction models, each one generated from a bootstrap sample of the available training set. We introduce an alternative method for bagging classification models, motivated by the reduced bootstrap methodology, where the generated bootstrap samples are forced to have a number of distinct original observations between two values k 1 and k 2 . Five choices for k 1 and k 2 are considered, and the five resulting models are empirically studied and compared with bagging on three real data sets, employing classification trees and neural networks as the base learners. This comparison reveals for this reduced bagging technique a trend to diminish the mean and the variance of the error rate.

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Machine learning‐based algorithms have been widely applied recently in different areas due to its ability to solve problems in all fields. In this research, machine learning techniques classifying the Bravais lattices from a conventional X‐ray diffraction diagram have been applied. Indexing algorithms are an essential tool of the preliminary protocol for the structural determination problem in crystallography. The task of reverting the obtained information in reciprocal lattice to direct space is a complex issue. As an alternative way to afford this problem, different machine learning algorithms have been applied and a comparison between them has been conducted. The obtained accuracy was 95.9% using 10‐fold cross‐validation (while the best result obtained so far has been 84%). A model based on Bragg positions was our unique predictor, allowing us to obtain the set of the interplanar lattice distances. Our model was successfully checked with a complex example. In addition, our procedure incorporates the following advantages: robustness versus imprecision in data acquisition and reduction of the amount of necessary input data. This is the first time so far that such classification has been carried out in true ab initio condition.

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