MCFS: Min-cut-based feature-selection
| Autor: | José A. Troyano, Fermín L. Cruz, Fernando Enríquez, F. Javier Ortega, Carlos G. Vallejo |
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| Rok vydání: | 2020 |
| Předmět: |
Information Systems and Management
business.industry Computer science Nearest neighbour Pattern recognition Feature selection 02 engineering and technology Directed graph Graph Management Information Systems Vertex (geometry) Max-flow min-cut theorem Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Low correlation business Software |
| Zdroj: | Knowledge-Based Systems. 195:105604 |
| ISSN: | 0950-7051 |
| DOI: | 10.1016/j.knosys.2020.105604 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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