Infinite Feature Selection: A Graph-based Feature Filtering Approach

06/15/2020
by   Giorgio Roffo, et al.
0

We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles. By two different interpretations (exploiting properties of power series of matrices and relying on Markov chains fundamentals) we can evaluate the values of paths (i.e., feature subsets) of arbitrary lengths, eventually go to infinite, from which we dub our framework Infinite Feature Selection (Inf-FS). Going to infinite allows to constrain the computational complexity of the selection process, and to rank the features in an elegant way, that is, considering the value of any path (subset) containing a particular feature. We also propose a simple unsupervised strategy to cut the ranking, so providing the subset of features to keep. In the experiments, we analyze diverse settings with heterogeneous features, for a total of 11 benchmarks, comparing against 18 widely-known comparative approaches. The results show that Inf-FS behaves better in almost any situation, that is, when the number of features to keep are fixed a priori, or when the decision of the subset cardinality is part of the process.

READ FULL TEXT
research
07/24/2017

Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach

Feature selection is playing an increasingly significant role with respe...
research
04/15/2019

Efficient Feature Selection of Power Quality Events using Two Dimensional (2D) Particle Swarms

A novel two-dimensional (2D) learning framework has been proposed to add...
research
11/24/2014

Mutual Information-Based Unsupervised Feature Transformation for Heterogeneous Feature Subset Selection

Conventional mutual information (MI) based feature selection (FS) method...
research
10/02/2019

Geometric Online Adaptation: Graph-Based OSFS for Streaming Samples

Feature selection seeks a curated subset of available features such that...
research
08/03/2018

A Two-Dimensional (2-D) Learning Framework for Particle Swarm based Feature Selection

This paper proposes a new generalized two dimensional learning approach ...
research
03/07/2014

Ant Colony based Feature Selection Heuristics for Retinal Vessel Segmentation

Features selection is an essential step for successful data classificati...

Please sign up or login with your details

Forgot password? Click here to reset