Hardware–software platform for computing irreducible testors

In pattern recognition, feature selection is a very important task for supervised classification. The problem consists in, given a dataset where each object is described by a set of features, finding a subset of the original features such that a classifier that runs on data containing only these features would reach high classification accuracy. A useful way to find this subset of the original features is through testor theory. A testor is defined as a subset of the original features that allows differentiating objects from different classes. Testors are very useful particularly when object descriptions contain both numeric and non-numeric features. Computing testors for feature selection is a very complex problem due to exponential complexity, with respect to the number of features, of algorithms based on testor theory. Hardware implementation of testor computing algorithms helps to improve their performance taking advantage of parallel processing for verifying if a feature subset is a testor in a single clock cycle. This paper introduces an efficient hardware–software platform for computing irreducible testors for feature selection in pattern recognition. Results of implementing the proposed platform using a FPGA-based prototyping board are presented and discussed.