Data driven feature learning

We present a regression-based feature learning algorithm that generates new features from a set of available features (raw data points). Being data-driven, it requires no domain knowledge and is hence generic. Such a representation is learnt by mining pairwise feature associations, identifying the linear or non-linear relationship between each pair, applying regression and selecting those relationships that are stable. Our experimental evaluation on 20 datasets taken from UC Irvine and Gene Expression, across different domains, provides evidence that the features learnt through our model can improve the overall prediction accuracy, substantially, over the original feature space across 8 different classifiers without any domain knowledge.