{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T12:59:40Z","timestamp":1777726780602,"version":"3.51.4"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031636455","type":"print"},{"value":"9783031636462","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-63646-2_23","type":"book-chapter","created":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T23:02:13Z","timestamp":1719183733000},"page":"354-370","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards Network Implementation of\u00a0CBR: Case Study of\u00a0a\u00a0Neural Network K-NN Algorithm"],"prefix":"10.1007","author":[{"given":"Xiaomeng","family":"Ye","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8666-3416","authenticated-orcid":false,"given":"David","family":"Leake","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ziwei","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"David","family":"Crandall","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"key":"23_CR1","volume-title":"Metric Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning","author":"A Bellet","year":"2015","unstructured":"Bellet, A., Habrard, A., Sebban, M.: Metric Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, New York (2015)"},{"key":"23_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/3-540-49257-7_15","volume-title":"Database Theory \u2014 ICDT\u201999","author":"K Beyer","year":"1999","unstructured":"Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is \u201cNearest Neighbor\u2019\u2019 meaningful? In: Beeri, C., Buneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217\u2013235. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/3-540-49257-7_15"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Bicego, M., Loog, M.: Weighted k-nearest neighbor revisited. In: Twenty-Third International Conference on Pattern Recognition (ICPR), pp. 1642\u20131647. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7899872"},{"key":"23_CR4","unstructured":"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.\u00a032. Curran (2019). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/adf7ee2dcf142b0e11888e72b43fcb75-Paper.pdf"},{"issue":"1","key":"23_CR5","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.autcon.2007.03.003","volume":"17","author":"J Chen","year":"2007","unstructured":"Chen, J., Hsu, S.C.: Hybrid ANN-CBR model for disputed change orders in construction projects. Autom. Constr. 17(1), 56\u201364 (2007)","journal-title":"Autom. Constr."},{"issue":"1","key":"23_CR6","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21\u201327 (1967)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"23_CR7","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"key":"23_CR8","unstructured":"Frankle, J., Carbin, M.: The lottery ticket hypothesis: finding sparse, trainable neural networks. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019). https:\/\/openreview.net\/forum?id=rJl-b3RcF7"},{"key":"23_CR9","series-title":"Lecture Notes in Computer Science()","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/978-3-031-40177-0_10","volume-title":"Case-Based Reasoning Research and Development","author":"L Gates","year":"2023","unstructured":"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.) Case-Based Reasoning Research and Development. Lecture Notes in Computer Science(), vol. 14141, pp. 153\u2013168. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-40177-0_10"},{"key":"23_CR10","unstructured":"Goldberger, J., Hinton, G.E., Roweis, S., Salakhutdinov, R.R.: Neighbourhood components analysis. In: Advances in Neural Information Processing Systems, vol.\u00a017. MIT Press (2004). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2004\/file\/42fe880812925e520249e808937738d2-Paper.pdf"},{"issue":"5","key":"23_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5), 1\u201342 (2018)","journal-title":"ACM Comput. Surv."},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Huang, J., Wei, Y., Yi, J., Liu, M.: An improved KNN based on class contribution and feature weighting. In: 2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), pp. 313\u2013316 (2018)","DOI":"10.1109\/ICMTMA.2018.00083"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Juarez, J.M., Craw, S., Lopez-Delgado, J.R., Campos, M.: Maintenance of case bases: Current algorithms after fifty years. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 5457\u20135463. IJCAI (2018)","DOI":"10.24963\/ijcai.2018\/770"},{"key":"23_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-3-030-29249-2_11","volume-title":"Case-Based Reasoning Research and Development","author":"MT Keane","year":"2019","unstructured":"Keane, M.T., Kenny, E.M.: How case-based reasoning explains neural networks: a theoretical analysis of XAI using Post-Hoc explanation-by-example from a survey of ANN-CBR twin-systems. In: Bach, K., Marling, C. (eds.) ICCBR 2019. LNCS (LNAI), vol. 11680, pp. 155\u2013171. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29249-2_11"},{"key":"23_CR15","first-page":"31","volume-title":"Case-Based Reasoning: Experiences, Lessons, and Future Directions","author":"J Kolodner","year":"1996","unstructured":"Kolodner, J., Leake, D.: A tutorial introduction to case-based reasoning. In: Leake, D. (ed.) Case-Based Reasoning: Experiences, Lessons, and Future Directions, pp. 31\u201365. AAAI Press, Menlo Park, CA (1996)"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Kulis, B.: Metric learning: a survey. Found. Trends Mach. Learn. 5, 287\u2013364 (2013). https:\/\/api.semanticscholar.org\/CorpusID:55485900","DOI":"10.1561\/2200000019"},{"key":"23_CR17","unstructured":"Leake, D.: CBR in context: The present and future. In: Leake, D. (ed.) Case-Based Reasoning: Experiences, Lessons, and Future Directions, pp. 3\u201330. AAAI Press, Menlo Park, CA (1996). http:\/\/www.cs.indiana.edu\/~leake\/papers\/a-96-01.html"},{"key":"23_CR18","unstructured":"Leake, D., Kinley, A., Wilson, D.: Learning to integrate multiple knowledge sources for case-based reasoning. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 246\u2013251. Morgan Kaufmann (1997)"},{"key":"23_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1007\/BFb0056333","volume-title":"Advances in Case-Based Reasoning","author":"DB Leake","year":"1998","unstructured":"Leake, D.B., Wilson, D.C.: Categorizing case-base maintenance: dimensions and directions. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS, vol. 1488, pp. 196\u2013207. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/BFb0056333"},{"key":"23_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/978-3-030-58342-2_22","volume-title":"Case-Based Reasoning Research and Development","author":"D Leake","year":"2020","unstructured":"Leake, D., Crandall, D.: On bringing case-based reasoning methodology to deep learning. In: Watson, I., Weber, R. (eds.) ICCBR 2020. LNCS (LNAI), vol. 12311, pp. 343\u2013348. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58342-2_22"},{"key":"23_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/978-3-030-86957-1_9","volume-title":"Case-Based Reasoning Research and Development","author":"D Leake","year":"2021","unstructured":"Leake, D., Ye, X.: Harmonizing case retrieval and\u00a0adaptation with alternating optimization. In: S\u00e1nchez-Ruiz, A.A., Floyd, M.W. (eds.) ICCBR 2021. LNCS (LNAI), vol. 12877, pp. 125\u2013139. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86957-1_9"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Li, O., Liu, H., Chen, C., Rudin, C.: Deep learning for case-based reasoning through prototypes: a neural network that explains its predictions. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 3530\u20133537. AAAI Press (2018)","DOI":"10.1609\/aaai.v32i1.11771"},{"key":"23_CR23","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s13748-019-00201-2","volume":"9","author":"BM Mathisen","year":"2019","unstructured":"Mathisen, B.M., Aamodt, A., Bach, K., Langseth, H.: Learning similarity measures from data. Prog. Artif. Intell. 9, 129\u2013143 (2019)","journal-title":"Prog. Artif. Intell."},{"key":"23_CR24","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1023\/B:APIN.0000043559.83167.3d","volume":"21","author":"J Park","year":"2004","unstructured":"Park, J., Im, K.H., Shin, C.K., Park, S.: MBNR: case-based reasoning with local feature weighting by neural network. Appl. Intell. 21, 265\u2013276 (2004)","journal-title":"Appl. Intell."},{"key":"23_CR25","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"23_CR26","unstructured":"Riegel, R., et\u00a0al.: Logical neural networks. arXiv preprint: arXiv:2006.13155 (2020)"},{"key":"23_CR27","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206\u2013215 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Sarabia, Y., Lorenzo, M., Perez, R., Martinez, R.: Extending CBR-ANN hybrid models using fuzzy sets. In: 2005 International Conference on Neural Networks and Brain, vol.\u00a03, pp. 1755\u20131760 (2005)","DOI":"10.1109\/ICNNB.2005.1614967"},{"key":"23_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0020597","volume-title":"Case-Based Reasoning: Experiences, Lessons, and Future Directions","author":"B Smyth","year":"1996","unstructured":"Smyth, B., Keane, M.: Design \u00e0 la D\u00e9j\u00e0 Vu: reducing the adaptation overhead. In: Leake, D. (ed.) Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press, Menlo Park, CA (1996)"},{"issue":"2","key":"23_CR30","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/S0004-3702(98)00059-9","volume":"102","author":"B Smyth","year":"1998","unstructured":"Smyth, B., Keane, M.: Adaptation-guided retrieval: questioning the similarity assumption in reasoning. Artif. Intell. 102(2), 249\u2013293 (1998)","journal-title":"Artif. Intell."},{"key":"23_CR31","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/978-3-030-29249-2_25","volume-title":"Case-Based Reasoning Research and Development","author":"JT Turner","year":"2019","unstructured":"Turner, J.T., Floyd, M.W., Gupta, K., Oates, T.: NOD-CC: a hybrid CBR-CNN architecture for novel object discovery. In: Bach, K., Marling, C. (eds.) ICCBR 2019. LNCS (LNAI), vol. 11680, pp. 373\u2013387. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29249-2_25"},{"key":"23_CR32","unstructured":"de\u00a0Vazelhes, W., Carey, C., Tang, Y., Vauquier, N., Bellet, A.: metric-learn: metric learning algorithms in python. J. Mach. Learn. Res. 21(138), 1\u20136 (2020). http:\/\/jmlr.org\/papers\/v21\/19-678.html"},{"key":"23_CR33","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207\u2013244 (2009)","journal-title":"J. Mach. Learn. Res."},{"issue":"1\u20135","key":"23_CR34","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1006593614256","volume":"11","author":"D Wettschereck","year":"1997","unstructured":"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\u20135), 273\u2013314 (1997)","journal-title":"Artif. Intell. Rev."},{"key":"23_CR35","series-title":"Lecture Notes in Computer Science()","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-031-14923-8_10","volume-title":"Case-Based Reasoning Research and Development","author":"X Ye","year":"2022","unstructured":"Ye, X., Leake, D., Crandall, D.: Case adaptation with neural networks: capabilities and limitations. In: Keane, M.T., Wiratunga, N. (eds.) Case-Based Reasoning Research and Development. Lecture Notes in Computer Science(), vol. 13405, pp. 143\u2013158. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-14923-8_10"},{"key":"23_CR36","unstructured":"Ye, X., Zhao, Z., Leake, D., Wang, X., Crandall, D.J.: Applying the case difference heuristic to learn adaptations from deep network features. CoRR abs\/2107.07095 (2021). https:\/\/arxiv.org\/abs\/2107.07095"}],"container-title":["Lecture Notes in Computer Science","Case-Based Reasoning Research and Development"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63646-2_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T13:42:25Z","timestamp":1732282945000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63646-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031636455","9783031636462"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63646-2_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"24 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Case-Based Reasoning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Merida","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccbr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccbr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}