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.
@article{arxiv.2206.07295,
title = {FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank},
author = {Huaduo Wang and Gopal Gupta},
journal= {arXiv preprint arXiv:2206.07295},
year = {2022}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2202.06913. text overlap with arXiv:2110.07843