Multi-class l(2)-Boost with the scoring coding

摘要

Boosting, one of the best off-the-shelf classification methods, has evoked widespread interest in machine learning and statistics. However, the original algorithm was developed for binary classification problems. In this paper, we study multi-class boosting algorithms under the l(2)-loss framework, and devise two multi-class l(2)-Boost algorithms. These are based on coordinate descent and gradient descent to minimize the multi-class l(2)-loss function. We derive a scoring coding scheme using optimal scoring constraints to encode class labels and a simple decoder to recover the true class labels. Our boosting algorithms are easily implemented and their results converge to the global optimum. Experiments with synthetic and real-world datasets show that, compared with several state-of-art methods, our algorithms provide more accurate results.