Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features

TL;DR

This paper combines deep learning for feature extraction with neural network adaptation learning in a case-based reasoning system, demonstrating effective adaptation knowledge acquisition from image data for age prediction.

Contribution

It introduces a two-phase approach integrating deep feature extraction with neural network learning for case adaptation, applicable to nonsymbolic data.

Findings

Successfully learned adaptation knowledge from image features.

Achieved better generalization on novel queries compared to baseline.

Performed comparably to a deep network regressor overall.

Abstract

The case difference heuristic (CDH) approach is a knowledge-light method for learning case adaptation knowledge from the case base of a case-based reasoning system. Given a pair of cases, the CDH approach attributes the difference in their solutions to the difference in the problems they solve, and generates adaptation rules to adjust solutions accordingly when a retrieved case and new query have similar problem differences. As an alternative to learning adaptation rules, several researchers have applied neural networks to learn to predict solution differences from problem differences. Previous work on such approaches has assumed that the feature set describing problems is predefined. This paper investigates a two-phase process combining deep learning for feature extraction and neural network based adaptation learning from extracted features. Its performance is demonstrated in a regression task on an image data: predicting age given the image of a face. Results show that the combined process can successfully learn adaptation knowledge applicable to nonsymbolic differences in cases. The CBR system achieves slightly lower performance overall than a baseline deep network regressor, but better performance than the baseline on novel queries.