Case-Based Reasoning
Developing case-based reasoning methods that learn useful representations for retrieving and adapting solutions from prior examples.
CbrCase Based Reasoning
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Publications
Learning Case Features with Proxy-Guided Deep Neural Networks
2025
Vibhas Kumar Vats, Zachary Wilkerson, Hiroki Sato, David Leake, David Crandall • ICCBR
This paper explores feature learning for case-based reasoning using deep neural networks. We introduce a training strategy that combines standard classification loss with additional objectives that better reflect how case-based systems compare examples. Experiments on multiple image datasets show that the learned features lead to stronger case-based classification than conventional end-to-end deep models.
CbrCase Based Reasoning
Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach
2024
Zachary Wilkerson, David Leake, Vibhas Vats, David Crandall • ICCBR
This paper examines whether transfer learning can help learn better indexing features for case-based reasoning in limited-data domains. Our experiments show that pretrained deep models produce stronger and more reliable features for case retrieval than models trained from scratch. In the tested setting, the resulting case-based classifier also performs better than similar deep learning baselines.
CbrCase Based Reasoning
Examining the Impact of Network Architecture on Extracted Feature Quality for CBR
2023
David Leake, Zachary Wilkerson, Vibhas Vats, Karan Acharya, David Crandall • ICCBR
This paper examines how different deep learning architectures influence feature extraction for case-based classification. By comparing multiple models, we show that the usefulness of extracted features depends on both the network design and the data available for training. The study also provides guidance for choosing suitable models for case-based reasoning tasks.
Case Based ReasoningCbr