论文标题
fold-tr：一种可扩展有效的诱导学习算法，用于学习排名
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank
论文作者
论文摘要
fold-r ++是一种用于二进制分类任务的新电感学习算法。它为混合类型（数值和分类）数据生成（可解释的）正常逻辑程序。我们提出了一种具有排名框架（称为fold-tr）的自定义的fold-r ++算法，该算法旨在按照培训数据中的排名模式对新项目进行排名。与fold-r ++一样，fold-tr算法能够直接处理混合型数据，并提供本机的理由来解释一对项目之间的比较。
FOLD-R++ is a new inductive learning algorithm for binary classification tasks. It generates an (explainable) normal logic program for mixed type (numerical and categorical) data. We present a customized FOLD-R++ algorithm with the ranking framework, called FOLD-TR, that aims to rank new items following the ranking pattern in the training data. Like FOLD-R++, the FOLD-TR algorithm is able to handle mixed-type data directly and provide native justification to explain the comparison between a pair of items.
