scikit-ika is an open source implementation of methods for handling recurrent concept drifts. It continuously models evolving data streams, providing accurate predictions in real time, using probabilistic networks and meta-information to proactively predict a change in the data stream.

Why

Many applications deal with data streams. Data streams can be perceived as a continuous sequence of data instances, often arriving at a high rate. In data streams, the underlying data distribution may change over time, causing decay in the predictive ability of the machine learning models. This phenomenon is known as concept drift.

Moreover, it is common for previously seen concepts to recur in real-world data streams, known as recurrent concept drifts. If a concept reappears, for example a particular weather pattern, previously learnt classifiers can be reused; thus the performance of the learning algorithm can be improved.

The Name

ika stands for any creatures that swims in water, or prized possession in Māori. This is exactly what our system does: ‘swim’ in data streams, and the learned knowledge is kept as the most prized possession.

Besides, ika stands for octopus in Japanese, which is where our logo comes from.

The Team

Dr. Yun Sing Koh and Prof. Gillian Dobbie and other hard working research fellows.