Optimizing Feature Sets for Structured Data

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Abstract

Choosing a suitable feature representation for structured data is a non-trivial task due to the vast number of potential candidates. Ideally, one would like to pick a small, but informative set of structural features, each providing complementary information about the instances. We frame the search for a suitable feature set as a combinatorial optimization problem. For this purpose, we define a scoring function that favors features that are as dissimilar as possible to all other features. The score is used in a stochastic local search (SLS) procedure to maximize the diversity of a feature set. In experiments on small molecule data, we investigate the effectiveness of a forward selection approach with two different linear classification schemes.