Infinite Feature Selection: A Graph-based Feature Filtering Approach

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...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 43; číslo 12; s. 4396 - 4410
Hlavní autoři: Roffo, Giorgio, Melzi, Simone, Castellani, Umberto, Vinciarelli, Alessandro, Cristani, Marco
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Computational complexity
Correlation
Feature extraction
Feature selection
filter methods
Filtration
Laplace equations
Markov chains
Markov processes
Mutual information
Power series
Redundancy
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí: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.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2020.3002843