Learning Concept Lengths Accelerates Concept Learning in ALC

TL;DR

This paper introduces CLIP, an extension of the ALC concept learning algorithm, which uses neural network-based concept length prediction to prune the search space, resulting in significantly faster runtimes and improved accuracy on benchmark datasets.

Contribution

The paper proposes a novel method to predict concept lengths using neural networks, enabling pruning of the search space in ALC-based concept learning, and extends CELOE to CLIP with substantial efficiency gains.

Findings

CLIP is at least 7.5 times faster than state-of-the-art algorithms.

Recurrent neural networks perform best for concept length prediction with F-measure 38%-92%.

CLIP improves F-measure on 3 out of 4 datasets.

Abstract

Concept learning approaches based on refinement operators explore partially ordered solution spaces to compute concepts, which are used as binary classification models for individuals. However, the number of concepts explored by these approaches can grow to the millions for complex learning problems. This often leads to impractical runtimes. We propose to alleviate this problem by predicting the length of target concepts before the exploration of the solution space. By these means, we can prune the search space during concept learning. To achieve this goal, we compare four neural architectures and evaluate them on four benchmarks. Our evaluation results suggest that recurrent neural network architectures perform best at concept length prediction with a macro F-measure ranging from 38% to 92%. We then extend the CELOE algorithm, which learns ALC concepts, with our concept length predictor. Our extension yields the algorithm CLIP. In our experiments, CLIP is at least 7.5 times faster than other state-of-the-art concept learning algorithms for ALC -- including CELOE -- and achieves significant improvements in the F-measure of the concepts learned on 3 out of 4 datasets. For reproducibility, we provide our implementation in the public GitHub repository at https://github.com/dice-group/LearnALCLengths.