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GRAPH-BASED FEATURE REPRESENTATION FOR MULTI-CLASS CLASSIFICATIONS USING THE JEFFRIES-MATUSITA DISTANCE

Visualization of the feature space has gained attention in recent years, especially with the growing importance of explainability in AI. When processing high-dimensional datasets, a common preprocessing 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. The Jeffries-Matusita (JM) distance is a measure for separability between two distributions that has been used for filter based feature selection. It calculates for each feature the separability strength between pairs of classes. In this work, a low-dimensional representation of the JM measures for the feature space is formed by diffusion map. Diffusion maps organizes the features by their class separation abilities. Moreover, feature elimination is performed based on the distribution of the points in the low-dimensional space. Experimental results are provided for 3 public datasets and compared with known filter-based feature selection techniques.

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