Feature learning in feature-sample networks using multi-objective optimization

TL;DR

This paper introduces an unsupervised feature learning method for binary datasets using feature-sample networks and multi-objective optimization, enhancing data representation for improved machine learning performance.

Contribution

It proposes a novel feature learning approach on feature-sample networks with multi-objective optimization, filling a gap in complex network-based data representation.

Findings

Enhanced networks contain more information

Improved performance of machine learning methods

Comparison of optimization strategies

Abstract

Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have been using complex networks for data representation and analysis. However, no feature learning method has been proposed for such category of techniques. Here, we present an unsupervised feature learning mechanism that works on datasets with binary features. First, the dataset is mapped into a feature--sample network. Then, a multi-objective optimization process selects a set of new vertices to produce an enhanced version of the network. The new features depend on a nonlinear function of a combination of preexisting features. Effectively, the process projects the input data into a higher-dimensional space. To solve the optimization problem, we design two metaheuristics based on the lexicographic genetic algorithm and the improved strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced network contains more information and can be exploited to improve the performance of machine learning methods. The advantages and disadvantages of each optimization strategy are discussed.