Sequence classification with side effect machines evolved via ring optimization

The explosion of available sequence data necessitates the development of sophisticated machine learning tools with which to analyze them. This study introduces a sequence-learning technology called side effect machines. It also applies a model of evolution which simulates the evolution of a ring species to the training of the side effect machines. A comparison is done between side effect machines evolved in the ring structure and side effect machines evolved using a standard evolutionary algorithm based on tournament selection. At the core of the training of side effect machines is a nearest neighbor classifier. A parameter study was performed to investigate the impact of the division of training data into examples for nearest neighbor assessment and training cases. The parameter study demonstrates that parameter setting is important in the baseline runs but had little impact in the ring-optimization runs. The ring optimization technique was also found to exhibit improved and also more reliable training performance. Side effect machines are tested on two types of synthetic data, one based on GC-content and the other checking for the ability of side effect machines to recognize an embedded motif. Three types of biological data are used, a data set with different types of immune-system genes, a data set with normal and retro-virally derived human genomic sequence, and standard and nonstandard initiation regions from the cytochrome-oxidase subunit one in the mitochondrial genome.