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MCFS: Min-cut-based feature-selection.
- Source :
-
Knowledge-Based Systems . May2020, Vol. 195, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- In this paper, MCFS (Min-Cut-based feature-selection) is presented, which is a feature-selection algorithm based on the representation of the features in a dataset by means of a directed graph. The main contribution of our work is to show the usefulness of a general graph-processing technique in the feature-selection problem for classification datasets. The vertices of the graphs used herein are the features together with two special-purpose vertices (one of which denotes high correlation to the feature class of the dataset, and the other denotes a low correlation to the feature class). The edges are functions of the correlations among the features and also between the features and the classes. A classic max-flow min-cut algorithm is applied to this graph. The cut returned by this algorithm provides the selected features. We have compared the results of our proposal with well-known feature-selection techniques. Our algorithm obtains results statistically similar to those achieved by the other techniques in terms of number of features selected, while additionally significantly improving the accuracy. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DIRECTED graphs
*GRAPH algorithms
*REPRESENTATIONS of graphs
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 195
- Database :
- Academic Search Index
- Journal :
- Knowledge-Based Systems
- Publication Type :
- Academic Journal
- Accession number :
- 142580112
- Full Text :
- https://doi.org/10.1016/j.knosys.2020.105604