Multimodal approach to feature extraction for image and signal learning problems
Damian R. Eads, Steven Williams, James P. Theiler, Reid B. Porter, Neal R. Harvey, Simon James Perkins, Steven P. Brumby, Nancy A. David · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
We present ZEUS, an algorithm for extracting features from images and time series signals. ZEIS is designed to solve a variety of machine learning problems including time series forecasting, signal classification, image and pixel classification of multispectral and panchromatic imagery. An evolutionary approach is used to extract features from a near-infinite space of possible combinations of nonlinear operators. Each problem type (i.e. signal or image, regression or classification, multiclass or binary) has its own set of primitive operators. We employ fairly generic operators, but note that the choice of which operators to use provides an opportunity to consult with a domain expert. Each feature is produced from a composition of some subset of these primitive operators. The fitness for an evolved set of features is given by the performance of a back-end classifier (or regressor) on training data. We demonstrate our multimodal approach to feature extraction on a variety of problems in remote sensing. The performance of this algorithm will be compared to standard approaches, and the relative benefit of various aspects of the algorithm will be investigated.