Deep Broad Learning - Big Models for Big Data

TL;DR

This paper introduces Deep Broad Learning (DBL), an algorithm that combines deep and broad modeling to effectively handle large-scale data with many features, achieving competitive accuracy.

Contribution

The paper proposes a novel Deep Broad Learning algorithm with a tunable depth parameter, enabling scalable out-of-core learning for big data applications.

Findings

DBL achieves accuracy comparable to state-of-the-art models.

DBL supports out-of-core learning for large datasets.

The model effectively integrates information from numerous features.

Abstract

Deep learning has demonstrated the power of detailed modeling of complex high-order (multivariate) interactions in data. For some learning tasks there is power in learning models that are not only Deep but also Broad. By Broad, we mean models that incorporate evidence from large numbers of features. This is of especial value in applications where many different features and combinations of features all carry small amounts of information about the class. The most accurate models will integrate all that information. In this paper, we propose an algorithm for Deep Broad Learning called DBL. The proposed algorithm has a tunable parameter $n$, that specifies the depth of the model. It provides straightforward paths towards out-of-core learning for large data. We demonstrate that DBL learns models from large quantities of data with accuracy that is highly competitive with the state-of-the-art.