A Clustering and Demotion Based Algorithm for Inductive Learning of Default Theories

TL;DR

This paper introduces Kmeans-FOLD, a novel clustering and demotion-based algorithm that enhances the induction of concise, understandable nonmonotonic logic programs from positive and negative examples, outperforming existing ILP systems.

Contribution

The paper presents a new algorithm combining K-means clustering and a demotion strategy to improve the induction of nonmonotonic logic programs, making them more concise and effective.

Findings

Significant improvement on datasets with multiple positive clusters.

Produced more concise and understandable logic programs.

Outperformed FOLD and ALEPH ILP systems in experiments.

Abstract

We present a clustering- and demotion-based algorithm called Kmeans-FOLD to induce nonmonotonic logic programs from positive and negative examples. Our algorithm improves upon-and is inspired by-the FOLD algorithm. The FOLD algorithm itself is an improvement over the FOIL algorithm. Our algorithm generates a more concise logic program compared to the FOLD algorithm. Our algorithm uses the K-means based clustering method to cluster the input positive samples before applying the FOLD algorithm. Positive examples that are covered by the partially learned program in intermediate steps are not discarded as in the FOLD algorithm, rather they are demoted, i.e., their weights are reduced in subsequent iterations of the algorithm. Our experiments on the UCI dataset show that a combination of K-Means clustering and our demotion strategy produces significant improvement for datasets with more than one cluster of positive examples. The resulting induced program is also more concise and therefore easier to understand compared to the FOLD and ALEPH systems, two state of the art inductive logic programming (ILP) systems.