Data Science and Mining team (DaSciM)
LIX Laboratory, École Polytechnique.

Software

Scikit-Multiflow: A Multi-label/multi-ouput framework for streaming data in Python

Scikit-Multiflow provides a Python framework for comparing, evaluating, and visualizing machine learning methods in a data-stream setting. Many methods from MEKA are reimplemented here (earlier, as part of the Molearn project). The Scikit-Learn framework supplies the ‘base models’.

MEKA: A Multi-label/multi-target Extension to weKA

I created and am main developer of the MEKA Project for multi-label classification: A Java framework containing around two dozen multi-label and multi-output classifiers for train/test, cross validation, incremental and semi-supervised learning.

MOA: Massive Online Analysis

I am involved in the MOA Project for learning in data streams.

ALife: Artificial Life environment

I created ALife: an artificial life environment for testing and comparing reinforcement learning algorithms with continous action and state space.

A2RMS: Implementation of the A2RMS Sampling Algorithm

A2RMS is a Matlab/GNU Octave implementation of the A2RMS sampling algorithm.

DPFlib: A library in C for distributed particle filters.

This is code for implementing distributed particle filters on very low powered sensor motes, in the TinyOS operating system. However, the code is also standalone and can be compiled and run by regular C. Here is a video of a deployment with a sensor testbed.

Datasets

Datasets which I have created (or parsed into a new format) are available at http://meka.sourceforge.net/#datasets; some of which are now also available from the mulan website.