Efficient Instance Selection Based on Spatial Abstraction

Machine learning approaches have been applied in huge volumes of data. In order to deal with this big data, techniques for instance selection have been applied for reducing the data to a manageable volume and, consequently, for reducing the computational resources that are necessary to apply machine learning approaches. In this paper, we propose an efficient approach for instance selection called ISDSP. It adopts the notion of spatial partition for efficiently splitting the dataset in sets of similar instances. In a second step, the algorithm selects a representative instance of each of the densest spatial partitions that were previously identified. The approach was evaluated on 15 well-known datasets used in a classification task, and its performance was compared to those of 6 state-of-the-art algorithms, considering two measures: accuracy and reduction. All the obtained results show that, in general, the proposed approach provides a good trade-off between accuracy and reduction, with a significantly lower running time, when compared with other approaches.