Ensemble- and Distance-Based Feature Ranking for Unsupervised Learning

TL;DR

This paper introduces two new unsupervised feature ranking methods, Genie3 score and URelief, which outperform existing techniques on benchmark datasets, with Genie3 showing the best overall predictive power.

Contribution

The paper presents novel ensemble- and distance-based unsupervised feature ranking methods, including an extension of Relief, with comprehensive analysis and a method to identify truly relevant features.

Findings

Genie3 score outperforms existing methods in predictive power.

URelief surpasses other unsupervised ranking algorithms.

Optimal hyper-parameter settings enhance method performance.

Abstract

In this work, we propose two novel (groups of) methods for unsupervised feature ranking and selection. The first group includes feature ranking scores (Genie3 score, RandomForest score) that are computed from ensembles of predictive clustering trees. The second method is URelief, the unsupervised extension of the Relief family of feature ranking algorithms. Using 26 benchmark data sets and 5 baselines, we show that both the Genie3 score (computed from the ensemble of extra trees) and the URelief method outperform the existing methods and that Genie3 performs best overall, in terms of predictive power of the top-ranked features. Additionally, we analyze the influence of the hyper-parameters of the proposed methods on their performance, and show that for the Genie3 score the highest quality is achieved by the most efficient parameter configuration. Finally, we propose a way of discovering the location of the features in the ranking, which are the most relevant in reality.