A new machine learning model based on the broad learning system and wavelets

In this work, we present a new neural network named WAvelet-Based Broad LEarning System (WABBLES). WABBLES is based on the flat structure of the broad learning system. Such structure offers an alternative to deep learning models, such as convolutional neural networks. The WABBLES network uses multiresolution analysis to look for subtle, yet important features from the input data for a better classification performance. WABBLES uses wavelets to map the input signal, to obtain more relevant features from it. This is achieved by autonomously learning and adjusting the dilation and translation parameters of a wavelet, which control its shape. In this way, the resulting mapping nodes have a better representation of the most important features for the classification problem. The construction of the model is described here, along with special considerations and algorithms involved. Finally, the proposed model is tested using a database of synthetic astronomical data and a benchmark dataset called the Breast Cancer Wisconsin Dataset (Original). The conducted experiments provide a comparison between the proposed model and several machine learning algorithms with different performance metrics applied to the context of exoplanet identification and breast cancer detection. Our results confirm that the WABBLES model obtains superior accuracy and F-score percentages than the other models.