Abstract
High-quality indices are essential for accurate retrieval in case-based reasoning. However, in some domains, indexing knowledge may be incomplete, unavailable, or unfeasible to obtain by knowledge acquisition, making knowledge-light machine learning methods an appealing alternative for generating indexing features. In response, previous work has developed promising methods for extracting indexing features from deep neural networks trained on case data. However, it has also underlined that CBR using features extracted from a deep neural network achieves low accuracy in domains for which the network itself has low accuracy when trained from scratch. This is a special concern for CBR feature extraction because the ability of CBR to reason successfully in “small-data" domains has been seen as one of its benefits. This paper reports on work investigating the hypothesis that transfer learning may help decrease the data requirements for index extraction. Specifically, it examines how model pretraining affects the quality of extracted indexing features for case-based classification, measured by the performance of a case-based classifier using those features for retrieval. Experimental results suggest that using a pretrained deep learning model for feature extraction can improve classification accuracy and consistency compared to using similar models trained from scratch. An unexpected and intriguing result is that the case-based classifier using extracted features outperformed analogous deep learning classifiers for the tested dataset.
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References
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–52 (1994)
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
Amin, K., Kapetanakis, S., Althoff, K.-D., Dengel, A., Petridis, M.: Answering with cases: a CBR approach to deep learning. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 15–27. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_2
Amin, K., Lancaster, G., Kapetanakis, S., Althoff, K.-D., Dengel, A., Petridis, M.: Advanced similarity measures using word embeddings and siamese networks in CBR. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) IntelliSys 2019. AISC, vol. 1038, pp. 449–462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29513-4_32
Bach, K., Mork, P.: On the explanation of similarity for developing and deploying CBR systems. In: Proceedings of the Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS 2020) (2020)
Barletta, R., Mark, W.: Explanation-based indexing of cases. In: Kolodner, J. (ed.) Proceedings of a Workshop on Case-Based Reasoning, pp. 50–60. DARPA, Morgan Kaufmann, Palo Alto (1988)
Barnett, A.J., et al.: Interpretable mammographic image classification using case-based reasoning and deep learning. In: Proceedings of IJCAI-21 Workshop on Deep Learning, Case-Based Reasoning, and AutoML (2021). https://arxiv.org/pdf/2107.05605
Bhatta, S., Goel, A.: Model-based learning of structural indices to design cases. In: Proceedings of the IJCAI-93 Workshop on Reuse of Design, pp. A1–A13. IJCAI, Chambery, France (1993)
Bonzano, A., Cunningham, P., Smyth, B.: Using introspective learning to improve retrieval in CBR: a case study in air traffic control. In: Leake, D.B., Plaza, E. (eds.) ICCBR 1997. LNCS, vol. 1266, pp. 291–302. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63233-6_500
Chai, J., Zeng, H., Li, A., Ngai, E.W.: Deep learning in computer vision: a critical review of emerging techniques and application scenarios. Mach. Learn. Appl. 6, 100134 (2021)
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., Su, J.K.: This looks like that: deep learning for interpretable image recognition. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8930–8941. Curran (2019)
Cox, M., Ram, A.: Introspective multistrategy learning: on the construction of learning strategies. Artif. Intell. 112(1–2), 1–55 (1999)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Domeshek, E.: Indexing stories as social advice. In: Proceedings of the Ninth National Conference on Artificial Intelligence, pp. 16–21. AAAI Press, Menlo Park (1991)
Fox, S., Leake, D.: Introspective reasoning for index refinement in case-based reasoning. J. Exp. Theor. Artif. Intell. 13(1), 63–88 (2001)
Gates, L., Leake, D., Wilkerson, K.: Cases are king: a user study of case presentation to explain CBR decisions. In: Massie, S., Chakraborti, S. (eds.) ICCBR 2023. LNCS, vol. 14141, pp. 153–168. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-40177-0_10
Goldstein, E., Kedar, S., Bareiss, R.: Easing the creation of a multipurpose case library. In: Proceedings of the AAAI-93 Workshop on Case-Based Reasoning, pp. 12–18. AAAI Press, Menlo Park (1993)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks (2016). https://arxiv.org/abs/1608.06993
Kenny, E.M., Keane, M.T.: Twin-systems to explain artificial neural networks using case-based reasoning: comparative tests of feature-weighting methods in ANN-CBR twins for XAI. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (2019)
Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53, 5455–5516 (2019)
Klenk, M., Aha, D.W., Molineaux, M.: The case for case-based transfer learning. AI Mag. 32(1), 54–69 (2011)
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: Proceedings of the 32nd International Conference on Machine Learning (2015)
Leake, D.: An indexing vocabulary for case-based explanation. In: Proceedings of the Ninth National Conference on Artificial Intelligence, pp. 10–15. AAAI Press, Menlo Park (1991)
Leake, D.: CBR in context: the present and future. In: Leake, D. (ed.) Case-Based Reasoning: Experiences, Lessons, and Future Directions, pp. 3–30. AAAI Press, Menlo Park, CA (1996). http://www.cs.indiana.edu/~leake/papers/a-96-01.html
Leake, D., Sooriamurthi, R.: Case dispatching versus case-base merging: when MCBR matters. Int. J. Artif. Intell. Tools 13(1), 237–254 (2004)
Leake, D., Wilkerson, Z., Crandall, D.: Extracting case indices from convolutional neural networks: a comparative study. In: Case-Based Reasoning Research and Development, ICCBR 2022 (2022)
Leake, D., Wilkerson, Z., Vats, V., Acharya, K., Crandall, D.: Examining the impact of network architecture on extracted feature quality for CBR. In: Massie, S., Chakraborti, S. (eds.) ICCBR 2023. LNCS, vol. 14141, pp. 3–18. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-40177-0_1
Leake, D., Ye, X.: Harmonizing case retrieval and adaptation with alternating optimization. In: Sánchez-Ruiz, A.A., Floyd, M.W. (eds.) ICCBR 2021. LNCS (LNAI), vol. 12877, pp. 125–139. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86957-1_9
Li, O., Liu, H., Chen, C., Rudin, C.: Deep learning for case-based reasoning through prototypes: a neural network that explains its predictions. https://arxiv.org/abs/1710.04806 (2018)
Martin, K., Wiratunga, N., Sani, S., Massie, S., Clos, J.: A convolutonal siamese network for developing similarity knowledge in the Selfback dataset. In: Proceedings of the International Conference on Case-Based Reasoning Workshops, CEUR Workshop Proceedings, ICCBR, pp. 85–94 (2017)
Oehlmann, R., Edwards, P., Sleeman, D.: Changing the viewpoint: re-indexing by introspective questioning. In: Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, Atlanta, GA (1994)
Ribani, R., Marengoni, M.: A survey of transfer learning for convolutional neural networks. In: 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pp. 47–57 (2019). https://doi.org/10.1109/SIBGRAPI-T.2019.00010
Sani, S., Wiratunga, N., Massie, S.: Learning deep features for KNN-based human activity recognition. In: 25th International conference on case-based reasoning (ICCBR 2017) (2017)
Sani, S., Wiratunga, N., Massie, S., Cooper, K.: Personalised human activity recognition using matching networks. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 339–353. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_23
Schank, R., et al.: Towards a general content theory of indices. In: Proceedings of the 1990 AAAI Spring Symposium on Case-Based Reasoning. AAAI Press, Menlo Park (1990)
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)
Turner, J.T., Floyd, M.W., Gupta, K.M., Aha, D.W.: Novel object discovery using case-based reasoning and convolutional neural networks. In: Case-Based Reasoning Research and Development, ICCBR 2018, pp. 399–414 (2018)
Turner, J.T., Floyd, M.W., Gupta, K.M., Oates, T.: NOD-CC: a hybrid CBR-CNN architecture for novel object discovery. In: Case-Based Reasoning Research and Development, ICCBR 2019, pp. 373–387 (2019)
Wettschereck, D., Aha, D., Mohri, T.: A review and empirical evaluation of feature-weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11(1–5), 273–314 (1997)
Wilkerson, Z., Leake, D., Crandall, D.: On combining knowledge-engineered and network-extracted features for retrieval. In: Case-Based Reasoning Research and Development, ICCBR 2021, pp. 248–262 (2021)
Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning - a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI) 41(9), 2251–2265 (2018)
Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43–76 (2021)
Acknowledgments
This work was funded by the US Department of Defense (Contract W52P1J2093009), and by the Department of the Navy, Office of Naval Research (Award N00014-19-1-2655). Authors Wilkerson and Vats led and contributed equivalently to system formulation/experiments, Wilkerson and Leake to paper formulation, and Crandall provided project guidance. We thank Karan Acharya for early contributions.
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Wilkerson, Z., Leake, D., Vats, V., Crandall, D. (2024). Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach. In: Recio-Garcia, J.A., Orozco-del-Castillo, M.G., Bridge, D. (eds) Case-Based Reasoning Research and Development. ICCBR 2024. Lecture Notes in Computer Science(), vol 14775. Springer, Cham. https://doi.org/10.1007/978-3-031-63646-2_10
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