Graph-based Extreme Feature Selection for Multi-class Classification Tasks
This work addresses feature selection for multi-class classification tasks, offering a domain-specific incremental improvement over existing filter-based methods.
The authors tackled the problem of feature selection for multi-class classification in high-dimensional data by proposing a graph-based filter method that combines Jeffries-Matusita distance and diffusion maps to drastically reduce features while preserving classification-relevant information, achieving competitive results compared to existing techniques.
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.