FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank

• URL: http://arxiv.org/abs/2206.07295v1
• Date: Wed, 15 Jun 2022 04:46:49 GMT
• Title: FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank
• Authors: Huaduo Wang and Gopal Gupta
• Abstract summary: FOLD-R++ is a new inductive learning algorithm for binary classification tasks. We present a customized FOLD-R++ algorithm with the ranking framework, called FOLD-TR.
• Score: 3.1981440103815717
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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