# BELIEF: A distance-based redundancy-proof feature selection method for Big Data

arXiv: https://arxiv.org/abs/1804.05774 · HTML: https://mlsanity.com/paper/1804.05774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Distributed Sparse Feature Selection in Communication-Restricted Networks](https://mlsanity.com/paper/2111.02802/) (2021)

## Abstract (arXiv)

With the advent of Big Data era, data reduction methods are highly demanded given its ability to simplify huge data, and ease complex learning processes. Concretely, algorithms that are able to filter relevant dimensions from a set of millions are of huge importance. Although effective, these techniques suffer from the "scalability" curse as well. In this work, we propose a distributed feature weighting algorithm, which is able to rank millions of features in parallel using large samples. This method, inspired by the well-known RELIEF algorithm, introduces a novel redundancy elimination measure that provides similar schemes to those based on entropy at a much lower cost. It also allows smooth scale up when more instances are demanded in feature estimations. Empirical tests performed on our method show its estimation ability in manifold huge sets --both in number of features and instances--, as well as its simplified runtime cost (specially, at the redundancy detection step).