Analysis of encoder representations as features using sparse autoencoders in gradient boosting and ensemble tree models

The performance of learning algorithms relies on factors such as the training strategy, the parameter tuning approach, and data complexity; in this scenario, extracted features play a fundamental role. Since not all the features maintain useful information, they can add noise, thus decreasing the performance of the algorithms. To address this issue, a variety of techniques such as feature ex-traction, feature engineering and feature selection have been developed, most of which fall into the unsupervised learning category. This study explores the generation of such features, using a set of k encoder layers, which are used to produce a low dimensional feature set F. The encoder layers were trained using a two-layer depth sparse autoencoder model, where PCA was used to estimate the right number of hidden units in the first layer. Then, a set of four algorithms, which belong to the gradient boosting and ensemble families were trained using the generated features. Finally, a performance comparison, using the encoder features against the original features was made. The results show that by using the reduced features it is possible to achieve equal or better results. Also, the approach improves more with highly imbalanced data sets.