Note

This repository is now archived and has been replaced with https://github.com/mrapp-ke/MLRL-Boomer.

This software package provides the official implementation of BOOMER - an algorithm for learning gradient boosted multi-label classification rules that integrates with the popular scikit-learn machine learning framework.

The goal of multi-label classification is the automatic assignment of sets of labels to individual data points, for example, the annotation of text documents with topics. The BOOMER algorithm uses gradient boosting to learn an ensemble of rules that is built with respect to a given multivariate loss function. To provide a versatile tool for different use cases, great emphasis is put on the efficiency of the implementation. To ensure its flexibility, it is designed in a modular fashion and can therefore easily be adjusted to different requirements.