Optimizing view generation in classification datasets

The divide-and-conquer strategy is a common approach for solving Computer Science problems, where one divides a problem into multiple potentially simpler subproblems whose results are combined. This decomposition approach can also help solve classification problems in Machine Learning (ML). In this paper, we propose a decomposition strategy to generate multiple views from a dataset in an optimized way. The objective is to divide the input features into subsets and build ML models for each. When a new instance has to be classified, the view for which the complexity in classifying the instance is lower is chosen to be used in prediction. We show experimentally how this approach can benefit the nearest neighbor classifier, increasing classification accuracy for complex problems while overcoming the limitations of this method in handling directly high-dimensional data.