Dimensionality Reduction using Symbolic Regression

摘要

In this paper, we propose a symbolic regression approach for data visualization that is suited for classification tasks. Our algorithm seeks a visually and semantically interpretable lower dimensional representation of the given dataset that would increase classifier accuracy as well. This simultaneous identification of easily interpretable dimensionality reduction and improved classification accuracy relieves the user of the burden of experimenting with the many combinations of classification and dimensionality reduction techniques.