A Construction of Robust Representations for Small Data Sets Using Broad Learning System

Abstract

Feature processing is an important step for modeling and can improve the accuracy of machine learning models. Feature extraction methods can effectively extract features from high-dimensional data sets and enhance the accuracy of tasks. However, the performance of feature extraction methods is not stable in low-dimensional data sets. This article extends the broad learning system (BLS) to a framework for constructing robust representations in low-dimensional and small data sets. First, the BLS changed from a supervised prediction method to an ensemble feature extraction method. Second, feature extraction methods instead of random mapping are used to generate mapped features. Third, deep representations, called enhancement features, are learned from the ensemble mapped features. Fourth, data for generating mapped features and enhancement features can be randomly selected. The ensemble of mapped features and enhancement features can provide robust representations to enhance the performance of downstream tasks. A label-based autoencoder (LA) is embedded in the BLS framework as an example to show the effectiveness of the framework. A random LA (RLA) is presented to generate more different features. The experimental results show that the BLS framework can construct robust representations and significantly promote the performance of machine learning models.