{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:18:41Z","timestamp":1761401921509},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,2,13]],"date-time":"2018-02-13T00:00:00Z","timestamp":1518480000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["New Gener. Comput."],"published-print":{"date-parts":[[2018,4]]},"DOI":"10.1007\/s00354-018-0031-9","type":"journal-article","created":{"date-parts":[[2018,2,13]],"date-time":"2018-02-13T15:24:01Z","timestamp":1518535441000},"page":"119-142","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Recursive Rule Extraction from NN using Reverse Engineering Technique"],"prefix":"10.1007","volume":"36","author":[{"given":"Manomita","family":"Chakraborty","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saroj Kr.","family":"Biswas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biswajit","family":"Purkayastha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,2,13]]},"reference":[{"issue":"1","key":"31_CR1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s00521-005-0002-1","volume":"15","author":"J Malone","year":"2006","unstructured":"Malone, J., McGarry, K., Wermter, S., Bowerman, C.: Data mining using rule extraction from Kohonen self-organising maps. Neural Comput. Appl. 15(1), 9\u201317 (2006)","journal-title":"Neural Comput. Appl."},{"key":"31_CR2","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2011","unstructured":"Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann Publishers, San Francisco (2011)","edition":"3"},{"issue":"2","key":"31_CR3","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s11063-011-9207-8","volume":"35","author":"MG Augasta","year":"2012","unstructured":"Augasta, M.G., Kathirvalavakumar, T.: Reverse engineering the NNs for rule extraction in classification problems. Neural Process. Lett. 35(2), 131\u2013150 (2012)","journal-title":"Neural Process. Lett."},{"issue":"2","key":"31_CR4","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1109\/72.839020","volume":"11","author":"R Setiono","year":"2000","unstructured":"Setiono, R.: Extracting M-of-N rules from trained NNs. IEEE Trans. Neural Netw. 11(2), 512\u2013519 (2000)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1","key":"31_CR5","first-page":"4","volume":"1","author":"K Jivani","year":"2014","unstructured":"Jivani, K., Ambasana, J., Kanani, S.: A survey on rule extraction approaches based techniques for data classification using NN. Int. J. Futuristic Trends Eng. Technol. 1(1), 4\u20137 (2014)","journal-title":"Int. J. Futuristic Trends Eng. Technol."},{"issue":"2","key":"31_CR6","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/TNN.2007.908641","volume":"19","author":"R Setiono","year":"2008","unstructured":"Setiono, R., Baesens, B., Mues, C.: Recursive NN rule extraction for data with mixed attributes. IEEE Trans. Neural Netw. 19(2), 299\u2013307 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"2\u20133","key":"31_CR7","first-page":"21l","volume":"13","author":"MW Craven","year":"1997","unstructured":"Craven, M.W., Shavlik, J.W.: Using NNs for data mining. Future Gen. Comput. Syst. 13(2\u20133), 21l\u2013229 (1997)","journal-title":"Future Gen. Comput. Syst."},{"issue":"8","key":"31_CR8","first-page":"1114","volume":"28","author":"LM Fu","year":"1994","unstructured":"Fu, L.M.: Rule generation from neural networks. IEEE Trans. Syst. Man Cybern. 28(8), 1114\u20131124 (1994)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"1","key":"31_CR9","first-page":"71","volume":"13","author":"G Towell","year":"1993","unstructured":"Towell, G., Shavlik, J.: The extraction of refined rules from knowledge based NNs. Mach. Learn. 13(1), 71\u2013101 (1993)","journal-title":"Mach. Learn."},{"issue":"3","key":"31_CR10","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1109\/2.485895","volume":"29","author":"R Setiono","year":"1996","unstructured":"Setiono, R., Liu, H.: Symbolic representation of NNs. IEEE Comput. 29(3), 71\u201377 (1996)","journal-title":"IEEE Comput."},{"key":"31_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0925-2312(97)00038-6","volume":"17","author":"R Setiono","year":"1997","unstructured":"Setiono, R., Liu, H.: NeuroLinear: from NNs to oblique decision rules. Neurocomputing 17, 1\u201324 (1997)","journal-title":"Neurocomputing"},{"issue":"3","key":"31_CR12","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/69.774103","volume":"11","author":"IA Taha","year":"1999","unstructured":"Taha, I.A., Ghosh, J.: Symbolic interpretation of artificial NNs. IEEE Trans. Knowl. Data Eng. 11(3), 448\u2013463 (1999)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"31_CR13","first-page":"1350","volume":"1","author":"SK Anbananthen","year":"2006","unstructured":"Anbananthen, S.K., Sainarayanan, G., Chekima, A., Teo, J.: Data mining using pruned artificial NN tree (ANNT). Inf. Commun. Technol. 1, 1350\u20131356 (2006)","journal-title":"Inf. Commun. Technol."},{"key":"31_CR14","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.procs.2013.09.299","volume":"20","author":"I Khan","year":"2013","unstructured":"Khan, I., Kulkarni, A.: Knowledge extraction from survey data using neural networks. Procedia Comput. Sci. 20, 433\u2013438 (2013)","journal-title":"Procedia Comput. Sci."},{"issue":"7","key":"31_CR15","first-page":"1020","volume":"21","author":"K Odajimaa","year":"2008","unstructured":"Odajimaa, K., Hayashi, Y., Tianxia, G., Setiono, R.: Greedy rule generation from discrete data and its use in NN rule extraction. NNs 21(7), 1020\u20131028 (2008)","journal-title":"NNs"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Wang, J.G., Yang, J.H., Zhang, W.X., Xu, J.W.: Rule extraction from artificial NN with optimized activation functions. In: IEEE 3rd International Conference on Intelligent System and Knowledge Engineering, pp. 873\u2013879. (2008)","DOI":"10.1109\/ISKE.2008.4731052"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Hara, A., Hayashi, Y.: Ensemble NN rule extraction using Re-RX algorithm. NNs (IJCNN) 1\u20136 (2012)","DOI":"10.1109\/IJCNN.2012.6252446"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Hayashi, Y., Sato, R., Mitra, S.: A new approach to three ensemble NN rule extraction using Recursive-Rule Extraction algorithm. NNs (IJCNN) 1\u20137 (2013)","DOI":"10.1109\/IJCNN.2013.6706823"},{"issue":"1\u20133","key":"31_CR19","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.neucom.2005.12.127","volume":"70","author":"ER Hruschka","year":"2006","unstructured":"Hruschka, E.R., Ebecken, N.F.F.: Extracting rules from multilayer perceptrons in classification problems: a clustering-based approach. Neurocomputing 70(1\u20133), 384\u2013397 (2006)","journal-title":"Neurocomputing"},{"issue":"2","key":"31_CR20","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1016\/j.eswa.2007.11.024","volume":"36","author":"H Kahramanli","year":"2009","unstructured":"Kahramanli, H., Allahverdi, N.: Rule extraction from trained adaptive NNs using artificial immune systems. Expert Syst. Appl. 36(2), 1513\u20131522 (2009)","journal-title":"Expert Syst. Appl."},{"key":"31_CR21","doi-asserted-by":"publisher","unstructured":"Setiono, R., Azcarraga, A., Hayashi, Y.: MofN rule extraction from neural networks trained with augmented discretized input. In: International Joint Conference on Neural Networks (IJCNN) 6\u201311 July, Beijing, China, pp. 1079\u20131086. (2014). https:\/\/doi.org\/10.1109\/ijcnn.2014.6889691","DOI":"10.1109\/ijcnn.2014.6889691"},{"key":"31_CR22","unstructured":"Sestito, S., Dillon, T.: Automated knowledge acquisition of rules with continuously valued attributes. In: Proceedings of 12th International Conference on Expert Systems and their Applications, pp. 645\u2013656. (1992)"},{"key":"31_CR23","first-page":"24","volume":"8","author":"M Craven","year":"1996","unstructured":"Craven, M., Shavlik, J.: Extracting tree-structured representations of trained network. Adv. Neural Inf. Process. Syst. (NIPS) 8, 24\u201330 (1996)","journal-title":"Adv. Neural Inf. Process. Syst. (NIPS)"},{"issue":"1","key":"31_CR24","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1162\/neco.1997.9.1.205","volume":"9","author":"R Setiono","year":"1997","unstructured":"Setiono, R.: Extracting rules from NNs by pruning and hidden-unit splitting. Neural Comput. 9(1), 205\u2013225 (1997)","journal-title":"Neural Comput."},{"key":"31_CR25","unstructured":"Liu, H., Tan, S.T.: X2R: A fast rule generator. In: Proceedings of the 7th IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 631\u2013635. (1995)"},{"issue":"2","key":"31_CR26","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/TNN.2005.863472","volume":"17","author":"TA Etchells","year":"2006","unstructured":"Etchells, T.A., Lisboa, P.J.G.: Orthogonal search-based rule extraction (OSRE) for trained NNs: a practical and efficient approach. IEEE Trans. NNs 17(2), 374\u2013384 (2006)","journal-title":"IEEE Trans. NNs"},{"key":"31_CR27","first-page":"3","volume":"26","author":"SK Biswas","year":"2017","unstructured":"Biswas, S.K., Chakraborty, M., Purkayastha, B., Thounaojam, D.M., Roy, P.: Rule extraction from training data using neural network. Int. J. Artif. Intell. Tool 26, 3 (2017)","journal-title":"Int. J. Artif. Intell. Tool"},{"issue":"11","key":"31_CR28","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TNNLS.2015.2389037","volume":"26","author":"EJ Fortuny de","year":"2015","unstructured":"de Fortuny, E.J., Martens, D.: Active learning-based pedagogical rule extraction. IEEE Trans. NNs Learn. Syst. 26(11), 2664\u20132677 (2015)","journal-title":"IEEE Trans. NNs Learn. Syst."},{"issue":"1","key":"31_CR29","first-page":"15","volume":"12","author":"S Rudy","year":"2000","unstructured":"Rudy, S., Kheng, W.: FERNN: an algorithm for fast extraction of rules from NNs. Appl. Intell. 12(1), 15\u201325 (2000)","journal-title":"Appl. Intell."},{"key":"31_CR30","doi-asserted-by":"crossref","unstructured":"Iqbal, R.A.: Eclectic rule extraction from NNs using aggregated decision trees. In: IEEE, 7th International Conference on Electrical & Computer Engineering (ICECE), pp. 129\u2013132 (2012)","DOI":"10.1109\/ICECE.2012.6471502"},{"key":"31_CR31","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.imu.2015.12.002","volume":"1","author":"Y Hayashi","year":"2016","unstructured":"Hayashi, Y., Nakano, S.: Use of a recursive-rule extraction algorithm with j48graft to archive highly accurate and concise rule extraction from a large breast cancer dataset. Inform. Med. Unlocked 1, 9\u201316 (2016)","journal-title":"Inform. Med. Unlocked"},{"key":"31_CR32","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.imu.2016.02.001","volume":"2","author":"Y Hayashi","year":"2016","unstructured":"Hayashi, Y., Yukita, S.: Rule extraction using recursive-rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset. Inform. Med. Unlocked 2, 92\u2013104 (2016)","journal-title":"Inform. Med. Unlocked"},{"key":"31_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.imu.2015.12.003","volume":"1","author":"Y Hayashi","year":"2015","unstructured":"Hayashi, Y., Nakano, S., Fujisawa, S.: Use of the recursive-rule extraction algorithm with continuous attributes to improve diagnostic accuracy in thyroid disease. Inform. Med. Unlocked. 1, 1\u20138 (2015)","journal-title":"Inform. Med. Unlocked."},{"key":"31_CR34","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.orp.2016.08.001","volume":"3","author":"Y Hayashi","year":"2016","unstructured":"Hayashi, Y.: Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective. Oper. Res. Perspect. 3, 32\u201342 (2016)","journal-title":"Oper. Res. Perspect."},{"issue":"1","key":"31_CR35","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1162\/neco.1997.9.1.185","volume":"9","author":"R Setiono","year":"1997","unstructured":"Setiono, R.: A penalty-function approach for pruning feedforward NNs. Neural Comput. 9(1), 185\u2013204 (1997)","journal-title":"Neural Comput."},{"key":"31_CR36","doi-asserted-by":"crossref","unstructured":"Permanasari, A.E., Rambli, D.R.A., Dominic, P.D.D.: Forecasting of salmonellosis incidence in human using artificial NN (ANN). In: Computer and Automation Engineering (ICCAE), the 2nd International Conference, vol. 1, pp. 136\u2013139. (2010)","DOI":"10.1109\/ICCAE.2010.5451981"},{"key":"31_CR37","unstructured":"Mondal, S.C., Mandal, P.: Application of artificial NN for modeling surface roughness in centerless grinding operation. In: 5th International & 26th All India Manufacturing Technology, Design and Research Conference, IIT Guwahati, Assam, India, 12\u201314 December 2014"}],"container-title":["New Generation Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00354-018-0031-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-018-0031-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-018-0031-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T22:17:53Z","timestamp":1570745873000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00354-018-0031-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,13]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,4]]}},"alternative-id":["31"],"URL":"https:\/\/doi.org\/10.1007\/s00354-018-0031-9","relation":{},"ISSN":["0288-3635","1882-7055"],"issn-type":[{"value":"0288-3635","type":"print"},{"value":"1882-7055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,13]]},"assertion":[{"value":"7 April 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}