Boosting instance selection algorithms

摘要

Instance selection is one of the most important preprocessing steps in many machine learning tasks. Due to the increasing size of the problems, removing useless, erroneous or noisy instances is frequently an initial step that is performed before other data mining algorithms are applied. Instance selection as part of this data reduction task is one of the most relevant problems in current data mining research.Over the past decades, many different instance selection algorithms have been proposed, each with its own strengths and weaknesses. However, as in the case of classification, it is unlikely that a single instance selection algorithm would be able to achieve good results across many different datasets and application fields. In classification, one of the most successful ways of consistently improving the performance of a single learner is the construction of ensembles using boosting methods. In this paper, we propose a novel approach for instance selection based on boosting instance selection algorithms in the same way boosting is applied to classification.The proposed approach opens a new field of research in which to apply the many techniques developed for boosting classifiers, for instance selection and other data reduction techniques such as feature selection and simultaneous instance and feature selection. Using 60 datasets for balanced problems and 45 datasets for class-imbalanced problems, the experiments reported show a clear improvement in several state-of-the-art instance selection algorithms using the proposed methodology.