Sparse tree-based initialization for neural networks

TL;DR

This paper introduces a sparse initialization method for neural networks based on tree ensemble insights, significantly improving performance on tabular data and rivaling gradient boosting methods.

Contribution

It presents a novel tree-based initialization technique for neural networks that enhances their performance on tabular data and maintains sparsity in weights.

Findings

Outperforms default MLP initialization on tabular datasets

Rivals complex deep learning solutions and gradient boosting

Preserves sparsity, acting as implicit regularization

Abstract

Dedicated neural network (NN) architectures have been designed to handle specific data types (such as CNN for images or RNN for text), which ranks them among state-of-the-art methods for dealing with these data. Unfortunately, no architecture has been found for dealing with tabular data yet, for which tree ensemble methods (tree boosting, random forests) usually show the best predictive performances. In this work, we propose a new sparse initialization technique for (potentially deep) multilayer perceptrons (MLP): we first train a tree-based procedure to detect feature interactions and use the resulting information to initialize the network, which is subsequently trained via standard stochastic gradient strategies. Numerical experiments on several tabular data sets show that this new, simple and easy-to-use method is a solid concurrent, both in terms of generalization capacity and computation time, to default MLP initialization and even to existing complex deep learning solutions. In fact, this wise MLP initialization raises the resulting NN methods to the level of a valid competitor to gradient boosting when dealing with tabular data. Besides, such initializations are able to preserve the sparsity of weights introduced in the first layers of the network through training. This fact suggests that this new initializer operates an implicit regularization during the NN training, and emphasizes that the first layers act as a sparse feature extractor (as for convolutional layers in CNN).