Similarity-margin based feature selection for symbolic interval data

In this paper we propose a feature selection method for symbolic interval data based on similarity margin. In this method, classes are parameterized by an interval prototype based on an appropriate learning process. A similarity measure is defined in order to estimate the similarity between the interval feature value and each class prototype. Then, a similarity margin concept has been introduced. The heuristic search is avoided by optimizing an objective function to evaluate the importance (weight) of each interval feature in a similarity margin framework. The experimental results show that the proposed method selects meaningful features for interval data. In particular, the method we propose yields a significant improvement on classification task of three real-world datasets.

Research highlights
► InterSym: Symbolic interval feature selection based on a similarity-margin concept.
► Definition of similarity-margin based objective function.
► Well established optimization of the objective function to avoid combinatorial search.
► Evaluate the interval feature importance within similarity-margin framework.
► InterSym robust against weakly relevant features and reduces significantly large datasets.