Infinite Feature Selection: A Graph-based Feature Filtering Approach
This work addresses feature selection for machine learning practitioners, offering a novel graph-based approach that improves performance in diverse settings, though it is incremental in its application of graph theory to an established problem.
The authors tackled the problem of feature selection by proposing a graph-based filtering framework that evaluates feature subsets as paths, allowing analysis of arbitrary lengths to rank features effectively. Their method outperformed 18 existing approaches across 11 benchmarks, showing superior performance in both fixed and adaptive subset selection scenarios.
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.