Welcome to GENDIS’s documentation!¶
In the time series classification domain, shapelets are small subseries that are discriminative for a certain class. It has been shown that by projecting the original dataset to a distance space, where each axis corresponds to the distance to a certain shapelet, classifiers are able to achieve state-of-the-art results on a plethora of datasets.
This repository contains an implementation of
GENDIS, an algorithm
that searches for a set of shapelets in a genetic fashion. The algorithm
is insensitive to its parameters (such as population size, crossover and
mutation probability, …) and can quickly extract a small set of
shapelets that is able to achieve predictive performances similar (or
better) to that of other shapelet techniques.