In many real classification problems a monotone relation between some predictors and the classes may be assumed when higher (or lower) values of those predictors are related to higher levels of the response. In this paper, we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules. These algorithms are based on theoretical developments that consider isotonic regression. We show the good performance of these procedures not only on simulations, but also on real data sets coming from two very different contexts, namely cancer diagnostic and failure of induction motors.

在许多实际分类问题中，当那些预测变量的较高（或较低）值与响应的较高级别相关时，可以假定某些预测变量与类别之间存在单调关系。在本文中，我们提出了一种新的基于LogitBoost的增强算法，该算法结合了这些等渗信息，从而产生更准确和易于解释的规则。这些算法基于考虑等渗回归的理论发展。我们不仅在仿真中而且在来自两个截然不同的环境（即癌症诊断和感应电动机故障）的真实数据集上均显示了这些程序的良好性能。