Feature learning with a genetic algorithm for fluorescence fingerprinting of plant species

Abstract

Proper feature analysis facilitates recognition by focusing the process to those characteristics of observed data that carry the most significant information for the given classification task. In this paper we address the problem of feature selection from a different point of view. Instead of searching for a feature subset out of a large set of predefined candidate features we consider the situation where, given the form of the features and an algorithm for extracting them from the data, the optimizer tunes the feature extraction parameters to improve class separability. This process of feature learning will be solved by the means of a genetic algorithm. The optimized feature set is subsequently used in a neural network classifier. The performance of the feature learning approach is demonstrated with the problem of automatic identification of plant species from their fluorescence induction curves. The general approach should also be useful with other pattern recognition problems where a priori unknown characteristics are extracted from a large feature space.