Graph-based Extreme Feature Selection for Multi-class Classification Tasks

• URL: http://arxiv.org/abs/2303.01792v1
• Date: Fri, 3 Mar 2023 09:06:35 GMT
• Title: Graph-based Extreme Feature Selection for Multi-class Classification Tasks
• Authors: Shir Friedman, Gonen Singer, Neta Rabin
• Abstract summary: This work focuses on a graph-based, filter feature selection method that is suited for multi-class classifications tasks. We aim to drastically reduce the number of selected features, in order to create a sketch of the original data that codes valuable information for the classification task.
• Score: 7.863638253070439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When processing high-dimensional datasets, a common pre-processing step is feature selection. Filter-based feature selection algorithms are not tailored to a specific classification method, but rather rank the relevance of each feature with respect to the target and the task. This work focuses on a graph-based, filter feature selection method that is suited for multi-class classifications tasks. We aim to drastically reduce the number of selected features, in order to create a sketch of the original data that codes valuable information for the classification task. The proposed graph-based algorithm is constructed by combing the Jeffries-Matusita distance with a non-linear dimension reduction method, diffusion maps. Feature elimination is performed based on the distribution of the features in the low-dimensional space. Then, a very small number of feature that have complementary separation strengths, are selected. Moreover, the low-dimensional embedding allows to visualize the feature space. Experimental results are provided for public datasets and compared with known filter-based feature selection techniques.

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