A Method Based on Genetic Programming for Improving the Quality of Datasets in Classification Problems
César Estébanez, Ricardo Aler, José M. Valls · 2007
The problem of the representation of data is a key issue in the Machine Learning (ML) field. ML tries to automatically induct knowledge from a set of examples or instances of a problem, learning how to distinguish between the different classes. It is known that inappropriate representations of the data can drastically limit the performance of ML algorithms. On the other hand, a high-quality representation of the same data, can produce a strong improvement in classification rates. In this work we present a GP-based method for automatically evolve projections. These projections change the data space of a classification problem into a higher-quality one, thus improving the performance of ML algorithms. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. We have tested our approach in four domains. The experiments show that it obtains good results, compared to other ML approaches that do not use our projections, while reducing dimensionality in many cases.