{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T10:11:43Z","timestamp":1769249503993,"version":"3.49.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T00:00:00Z","timestamp":1667865600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T00:00:00Z","timestamp":1667865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Defense Industrial Technology Development Program","award":["JCKY2020601B018"],"award-info":[{"award-number":["JCKY2020601B018"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Fuzzy Syst."],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s40815-022-01392-y","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T10:51:42Z","timestamp":1667904702000},"page":"575-600","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Research on a Kind of Multi-objective Evolutionary Fuzzy System with a Flowing Data Pool and a Rule Pool for Interpreting Neural Networks"],"prefix":"10.1007","volume":"25","author":[{"given":"Ke","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Ning","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Han","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,8]]},"reference":[{"issue":"1","key":"1392_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1049\/cit2.12028","volume":"6","author":"A Chakraborty","year":"2021","unstructured":"Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., Mukhopadhyay, D.: Adversarial attacks and defences: a survey. CAAI Trans. Intell. Technol. 6(1), 25\u201345 (2021)","journal-title":"CAAI Trans. Intell. Technol."},{"key":"1392_CR2","first-page":"31","volume-title":"Automatic classification of exudates in color fundus images using an augmented deep learning procedure","author":"L Wang","year":"2019","unstructured":"Wang, L., Huang, Y., Lin, B., Wu, W., Chen, H., Pu, J.: Automatic classification of exudates in color fundus images using an augmented deep learning procedure. In: Proc. of the Third Int Symp on Image Comput and Digit Med. pp. 31\u201335 (2019)"},{"key":"1392_CR3","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.neunet.2021.04.017","volume":"141","author":"G Li","year":"2021","unstructured":"Li, G., Liang, S., Nie, S., Liu, W., Yang, Z.: Deep neural network-based generalized sidelobe canceller for dual-channel far-field speech recognition. Neural Netw. 141, 225\u2013237 (2021)","journal-title":"Neural Netw."},{"key":"1392_CR4","doi-asserted-by":"publisher","first-page":"199","DOI":"10.3389\/fnins.2020.00199","volume":"14","author":"J Wu","year":"2020","unstructured":"Wu, J., Ylmaz, E., Zhang, M., Li, H., Tan, K.C.: Deep spiking neural networks for large vocabulary automatic speech recognition. Front. Neurosci. 14, 199 (2020)","journal-title":"Front. Neurosci."},{"issue":"1","key":"1392_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3365211","volume":"38","author":"F Ahmad","year":"2020","unstructured":"Ahmad, F., Abbasi, A., Li, J., Dobolyi, D.G., Netemeyer, R.G., Clifford, G.D., Chen, H.: A deep learning architecture for psychometric natural language processing. ACM Trans. Inf. Syst. 38(1), 1\u201329 (2020)","journal-title":"ACM Trans. Inf. Syst."},{"key":"1392_CR6","doi-asserted-by":"publisher","first-page":"2758","DOI":"10.1109\/TIP.2021.3051756","volume":"30","author":"M Gu","year":"2021","unstructured":"Gu, M., Zhao, Z., Jin, W., Hong, R., Wu, F.: Graph-based multi-interaction network for video question answering. IEEE Trans. Image Process. 30, 2758\u20132770 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"1392_CR7","unstructured":"Zhang, Y., Ti\u0148o, P., Leonardis, A., Tang, K.: A survey on neural network interpretability. arXiv:2012.14261 (2020)"},{"key":"1392_CR8","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv:1702.08608 (2017)"},{"key":"1392_CR9","unstructured":"Samek, W. Wiegand, T., M\u00fcller, K.R.: Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. Int. Telecommun. Union. arXiv:1708.08296 (2017)"},{"issue":"7623","key":"1392_CR10","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/538020a","volume":"538","author":"D Castelvecchi","year":"2016","unstructured":"Castelvecchi, D.: Can we open the black box of AI? Nature 538(7623), 20\u201323 (2016)","journal-title":"Nature"},{"issue":"11","key":"1392_CR11","doi-asserted-by":"publisher","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","volume":"32","author":"E Tjoa","year":"2020","unstructured":"Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (XAI): towards medical XAI. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 4793\u20134813 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1392_CR12","doi-asserted-by":"crossref","unstructured":"Boz, O.: Extracting decision trees from trained neural networks. In: Proc. of the Eighth ACM SIGKDD Int Conf on Knowl Discov and Data Min. 32 (12), 1999\u20132009 (2002)","DOI":"10.1016\/S0031-3203(98)00181-2"},{"key":"1392_CR13","doi-asserted-by":"crossref","unstructured":"Wu, M., Parbhoo, S., Hughes, M.C., Kindle, R., Celi, L.A., Zazzi, M., Roth, V., Doshi-Velez, F.: Regional tree regularization for interpretability in black box models. In: Proc. of the AAAI Conf on Artif Intell 34(4) 6413\u20136421 (2020)","DOI":"10.1609\/aaai.v34i04.6112"},{"key":"1392_CR14","doi-asserted-by":"crossref","unstructured":"Wu, M., Hughes, M.C., Parbhoo, S., Zazzi, M., Roth, V., DoshiVelez, F.: Beyond sparsity: tree regularization of deep models for interpretability. In: Proc of 32nd AAAI Conf on Artif Intell, New Orleans, LA, pp. 1670\u20131678, (2018)","DOI":"10.1609\/aaai.v32i1.11501"},{"issue":"7","key":"1392_CR15","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1016\/j.neunet.2008.01.003","volume":"21","author":"K Odajima","year":"2008","unstructured":"Odajima, K., Hayashi, Y., Tianxia, G., Setiono, R.: Greedy rule generation from discrete data and its use in neural network rule extraction. Neural Netw. 21(7), 1010\u20131028 (2008)","journal-title":"Neural Netw."},{"issue":"4","key":"1392_CR16","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1016\/j.neunet.2009.02.001","volume":"22","author":"R Nayak","year":"2009","unstructured":"Nayak, R.: Generating rules with predicates, terms and variables from the pruned neural networks. Neural Netw. 22(4), 405\u2013414 (2009)","journal-title":"Neural Netw."},{"issue":"5","key":"1392_CR17","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1109\/72.623216","volume":"8","author":"JM Benitez","year":"1997","unstructured":"Benitez, J.M., Castro, J.L., Requena, I.: Are artificial neural networks black boxes? IEEE Trans. Neural Netw. 8(5), 1156\u20131164 (1997)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1","key":"1392_CR18","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/72.977279","volume":"13","author":"JL Castro","year":"2002","unstructured":"Castro, J.L., Mantas, C.J., Benitez, J.M.: Interpretation of artificial neural networks by means of fuzzy rules. IEEE Trans. Neural Netw. 13(1), 101\u2013116 (2002)","journal-title":"IEEE Trans. Neural Netw."},{"key":"1392_CR19","unstructured":"Wang, T.: Gaining free or low-cost transparency with interpretable partial substitute. In Proc. of the 36th Int Conf on Mach Learn, PMLR, vol. 97, pp. 6505\u20136514, 2019."},{"key":"1392_CR20","doi-asserted-by":"crossref","unstructured":"Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: Proc. of the IEEE Conf on Comput Vis and Pattern Recognit, (CVPR), pp. 3319\u20133327 (2017)","DOI":"10.1109\/CVPR.2017.354"},{"key":"1392_CR21","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv:1312.6034 (2013)"},{"key":"1392_CR22","unstructured":"Nguyen, A.M., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J.: Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Proc. of the 30th Int Conf on Neural Inf Process Syst, pp. 3395\u20133403 (2016)"},{"key":"1392_CR23","doi-asserted-by":"crossref","unstructured":"Dalvi, F., Durrani, N., Sajjad, H., Belinkov, Y., Bau, D.A., Glass, J.: What is one grain of sand in the desert? analyzing individual neurons in deep nlp models. In: Proc. of the AAAI Conf on Artif Intell (AAAI), vol. 33, pp. 6309\u20136317 (2019)","DOI":"10.1609\/aaai.v33i01.33016309"},{"issue":"2","key":"1392_CR24","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vision 128(2), 336\u2013359 (2020)","journal-title":"Int. J. Comput. Vision"},{"key":"1392_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proc. of the IEEE Conf on Comput Vis and Pattern Recognit (CVPR), pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"1392_CR26","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Not Just a Black Box: Learning important features through propagating activation differences. arXiv:1605.01713 (2016)"},{"key":"1392_CR27","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. arXiv:1810.03292 (2018)"},{"key":"1392_CR28","unstructured":"Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., Lee, S.: Counterfactual visual explanations. In: Proc. of the 36th Int Conf on Mach Learn. 97 2376\u20132384 (2019)"},{"key":"1392_CR29","unstructured":"Zintgraf, L.M., Cohen, T.S., Adel, T., Welling, M.: Visualizing deep neural network decisions: prediction difference analysis. arXiv:1702.04595 (2017)"},{"key":"1392_CR30","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. arXiv:1705.07874v2 (2017)"},{"key":"1392_CR31","unstructured":"Heskes, T., Sijben, E., Bucur, I.G., Claassen, T.: Causal Shapley values: exploiting causal knowledge to explain individual predictions of complex models. In: Proc. of the 34th Conf on Neural Inf Process Syst (NeurIPS), 33, (2020)"},{"key":"1392_CR32","unstructured":"Chen, J., Song, L., Wainwright, M., Jordan, M.: Learning to explain: an information-theoretic perspective on model interpretation. In: Proc. of the 35th Int Conf on Mach Learn, Stockholm, Swede. 80, (2018)"},{"key":"1392_CR33","unstructured":"Ghorbani, A., Wexler, J., Zou, J.Y., Kim, B.: Towards automatic concept-based explanations. In: Proc. of the 33th Conf on Neural Inf Process Syst (NeurIPS), pp. 9277\u20139286 (2019)"},{"key":"1392_CR34","unstructured":"Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: Proc. of the 34th Int Conf on Mach Learn, 70, 1885\u20131894 (2017)"},{"key":"1392_CR35","unstructured":"Yeh, C.K., Kim, J., Yen, I.E.-H., Ravikumar, P.K.: Representer point selection for explaining deep neural networks. In: Proc. of the 32nd Int Confe on Neural Inf Process Syst, pp. 9311\u20139321 (2018)"},{"key":"1392_CR36","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: Proc. of the 32nd AAAI Conf on Artif Intel, 32(1), 3530\u20133537 (2018)","DOI":"10.1609\/aaai.v32i1.11771"},{"key":"1392_CR37","unstructured":"Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., Su, J.K.: This looks like that: deep learning for interpretable image recognition. arXiv:1806.10574 (2018)"},{"issue":"3","key":"1392_CR38","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338\u2013353 (1965)","journal-title":"Inf. Control"},{"key":"1392_CR39","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.neucom.2004.04.015","volume":"63","author":"UM Kaczmar","year":"2005","unstructured":"Kaczmar, U.M., Trelak, W.: Fuzzy logic and evolutionary algorithm\u2014two techniques in rule extraction from neural networks. Neurocomputing 63, 359\u2013379 (2005)","journal-title":"Neurocomputing"},{"key":"1392_CR40","doi-asserted-by":"crossref","unstructured":"Craven, M.W., Shavlik, J.W.: Using sampling and queries to extract rules from trained neural networks. In: Proc. of the Eleventh Int Conf, pp. 37\u201345 (1994)","DOI":"10.1016\/B978-1-55860-335-6.50013-1"},{"key":"1392_CR41","doi-asserted-by":"crossref","unstructured":"Saito, K., Nakano, R.: Medical diagnostic expert system based on PDP model. In: Proc. IEEE Int Conf on Neural Netwo, pp. 255\u2013262 (2002)","DOI":"10.1109\/ICNN.1988.23855"},{"issue":"11","key":"1392_CR42","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1109\/TFUZZ.2019.2896852","volume":"27","author":"YW Kerk","year":"2019","unstructured":"Kerk, Y.W., Tay, K.M., Lim, C.P.: Monotone interval fuzzy inference systems. IEEE Trans. Fuzzy Syst. 27(11), 2255\u20132264 (2019)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"10","key":"1392_CR43","doi-asserted-by":"publisher","first-page":"2067","DOI":"10.1109\/TFUZZ.2019.2893365","volume":"27","author":"Y Sheng","year":"2019","unstructured":"Sheng, Y., Lewis, F.L., Zeng, Z., Huang, T.: Stability and stabilization of Takagi-Sugeno fuzzy systems with hybrid time-varying delays. IEEE Trans. Fuzzy Syst. 27(10), 2067\u20132078 (2019)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"4","key":"1392_CR44","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1109\/TFUZZ.2019.2916103","volume":"28","author":"JM Mendel","year":"2020","unstructured":"Mendel, J.M., Chimatapu, R., Hagras, H.: Comparing the performance potentials of singleton and non-singleton type-1 and interval type-2 fuzzy systems in terms of sculpting the state space. IEEE Trans. Fuzzy Syst. 28(4), 783\u2013794 (2020)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"1392_CR45","doi-asserted-by":"publisher","first-page":"126066","DOI":"10.1109\/ACCESS.2020.3008064","volume":"8","author":"M Mazandarani","year":"2020","unstructured":"Mazandarani, M., Li, X.: Fractional fuzzy inference system: the new generation of fuzzy inference systems. IEEE Access 8, 126066\u2013126082 (2020)","journal-title":"IEEE Access"},{"key":"1392_CR46","unstructured":"Smith, S.F.: A learning system based on genetic adaptive algorithms. University of Pittsburgh. ProQuest Dissertations Publishing (1980)"},{"key":"1392_CR47","doi-asserted-by":"crossref","unstructured":"Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Pattern-Directed Inference Syst, pp. 313\u2013329 (1978)","DOI":"10.1016\/B978-0-12-737550-2.50020-8"},{"key":"1392_CR48","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-3-642-01799-5_5","volume":"1","author":"H Ishibuchi","year":"2009","unstructured":"Ishibuchi, H., Nojima, Y.: Multiobjective genetic fuzzy systems. Comput. Intell. 1, 131\u2013173 (2009)","journal-title":"Comput. Intell."},{"issue":"4","key":"1392_CR49","first-page":"444","volume":"5","author":"ZY Xing","year":"2007","unstructured":"Xing, Z.Y., Yong, Z., Hou, Y.L., Jia, L.M.: On generating fuzzy systems based on Pareto multi-objective cooperative coevolutionary algorithm. Int. J. Control Autom. Syst. 5(4), 444\u2013455 (2007)","journal-title":"Int. J. Control Autom. Syst."},{"issue":"1","key":"1392_CR50","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/S0165-0114(03)00113-1","volume":"141","author":"F Hoffmann","year":"2004","unstructured":"Hoffmann, F.: Combining boosting and evolutionary algorithms for learning of fuzzy classification rules. Fuzzy Sets Syst. 141(1), 47\u201358 (2004)","journal-title":"Fuzzy Sets Syst."},{"issue":"2","key":"1392_CR51","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/S0165-0114(99)00095-0","volume":"120","author":"L Castillo","year":"2001","unstructured":"Castillo, L., Gonzalez, A., Perez, R.: Including a simplicity criterion in the selection of the best rule in a genetic algorithm. Fuzzy Sets Syst. 120(2), 309\u2013321 (2001)","journal-title":"Fuzzy Sets Syst."},{"issue":"1","key":"1392_CR52","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/S0165-0114(98)00179-1","volume":"112","author":"TP Hong","year":"2000","unstructured":"Hong, T.P., Chen, J.-B.: Processing individual fuzzy attributes for fuzzy rule induction. Fuzzy Sets Syst. 112(1), 127\u2013140 (2000)","journal-title":"Fuzzy Sets Syst."},{"key":"1392_CR53","volume-title":"A Course in Fuzzy Systems and Control","author":"LX Wang","year":"1997","unstructured":"Wang, L.X.: A Course in Fuzzy Systems and Control. Prentice Hall, Englewood Cliffs (1997)"},{"issue":"6","key":"1392_CR54","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1109\/21.199466","volume":"22","author":"LX Wang","year":"1992","unstructured":"Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414\u20131427 (1992)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"8","key":"1392_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40815-020-00954-2","volume":"22","author":"K Zhang","year":"2020","unstructured":"Zhang, K., Hao, W.N., Yu, X.H., Jin, D.W., Zhang, Z.H.: A multitasking genetic algorithm for Mamdani fuzzy system with fully overlapping triangle membership functions. Int. J. Fuzzy Syst. 22(8), 1\u201317 (2020)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"6","key":"1392_CR56","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1109\/TFUZZ.2003.819839","volume":"11","author":"LX Wang","year":"2003","unstructured":"Wang, L.X.: The WM method completed: a flexible fuzzy system approach to data mining. IEEE Trans. Fuzzy Syst. 11(6), 768\u2013782 (2003)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"1392_CR57","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/s40815-020-00998-4","volume":"23","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Chen, D., Zhao, W., Mo, H.: Deep fuzzy system algorithms based on deep learning and input sharing for regression application. Int. J. Fuzzy Syst. 23, 727\u2013742 (2021). https:\/\/doi.org\/10.1007\/s40815-020-00998-4","journal-title":"Int. J. Fuzzy Syst."},{"issue":"3","key":"1392_CR58","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.1016\/j.eswa.2007.02.011","volume":"34","author":"PC Chang","year":"2008","unstructured":"Chang, P.C., Liu, C.H., Lai, R.K.: A fuzzy case-based reasoning model for sales forecasting in print circuit board industries. Expert Syst. Appl. 34(3), 2049\u20132058 (2008)","journal-title":"Expert Syst. Appl."},{"key":"1392_CR59","doi-asserted-by":"publisher","DOI":"10.1007\/s40815-022-01329-5","author":"D Chen","year":"2022","unstructured":"Chen, D., Tong, W., Huang, Y., Zhang, J.: FLOWFS: fast learning-algorithm with optimal weights for fuzzy systems. Int. J. Fuzzy Syst. (2022). https:\/\/doi.org\/10.1007\/s40815-022-01329-5","journal-title":"Int. J. Fuzzy Syst."},{"issue":"1","key":"1392_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0165-0114(95)00302-9","volume":"84","author":"Y Yufei","year":"1996","unstructured":"Yufei, Y., Huijun, Z.: A genetic algorithm for generating fuzzy classification rules. Fuzzy Sets Syst. 84(1), 1\u201319 (1996)","journal-title":"Fuzzy Sets Syst."},{"key":"1392_CR61","doi-asserted-by":"crossref","unstructured":"Ishibuchi, H., Nojima, Y., Kuwajima, I.: Fuzzy data mining by heuristic rule extraction and multiobjective genetic rule selection. In: Proc. IEEE Int Conf on IEEE Fuzzy Syst, pp. 1633\u20131640 (2006)","DOI":"10.1109\/FUZZY.2006.1681926"},{"key":"1392_CR62","doi-asserted-by":"crossref","unstructured":"Zhang, P., Shen, Q.: A novel framework of fuzzy rule interpolation for Takagi Sugeno-Kang inference systems. In: Proc. 2019 IEEE Int Conf on Fuzzy Syst (FUZZ-IEEE), pp. 1\u20136 (2019)","DOI":"10.1109\/FUZZ-IEEE.2019.8858833"},{"issue":"1","key":"1392_CR63","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/TFUZZ.2012.2201338","volume":"21","author":"M Fazzolari","year":"2013","unstructured":"Fazzolari, M., Alcala, R., Nojima, Y., Ishibuchi, H., Herrera, F.: A review of the application of multi-objective evolutionary systems: current status and further directions. IEEE Trans. Fuzzy Syst. 21(1), 45\u201365 (2013)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"20","key":"1392_CR64","doi-asserted-by":"publisher","first-page":"4340","DOI":"10.1016\/j.ins.2011.02.021","volume":"181","author":"MJ Gacto","year":"2011","unstructured":"Gacto, M.J., Alcal\u00e1, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181(20), 4340\u20134360 (2011)","journal-title":"Inf. Sci."},{"issue":"10","key":"1392_CR65","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1007\/s00500-010-0628-5","volume":"15","author":"JM Alonso","year":"2011","unstructured":"Alonso, J.M., Magdalena, L.: HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers. Soft. Comput. 15(10), 1959\u20131980 (2011)","journal-title":"Soft. Comput."},{"key":"1392_CR66","doi-asserted-by":"publisher","DOI":"10.1007\/s40815-022-01324-w","author":"C Biedma-Rdguez","year":"2022","unstructured":"Biedma-Rdguez, C., Gacto, M.J., Anguita-Ruiz, A., Alcal\u00e1-Fdez, J., Alcal, R.: Transparent but accurate evolutionary regression combining new linguistic fuzzy grammar and a novel interpretable linear extension. Int. J. Fuzzy Syst. (2022). https:\/\/doi.org\/10.1007\/s40815-022-01324-w","journal-title":"Int. J. Fuzzy Syst."},{"key":"1392_CR67","doi-asserted-by":"crossref","unstructured":"Marquez, A., M\u00e1rquez, F., Peregrin, A.: A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification. In: Proc. of IEEE World Congress on Comput Intell, pp. 277\u2013283 (2010)","DOI":"10.1109\/FUZZY.2010.5584294"},{"issue":"4","key":"1392_CR68","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.ijar.2010.11.007","volume":"52","author":"C Mencar","year":"2011","unstructured":"Mencar, C., Castiello, C., Cannone, R., Fanelli, A.M.: Interpretability assessment of fuzzy knowledge bases: a cointension based approach. Int. J. Approx. Reason. 52(4), 501\u2013518 (2011)","journal-title":"Int. J. Approx. Reason."},{"key":"1392_CR69","doi-asserted-by":"publisher","first-page":"1938","DOI":"10.1007\/s40815-018-0478-3","volume":"20","author":"H Kalia","year":"2018","unstructured":"Kalia, H., Dehuri, S., Ghosh, A., Cho, S.B.: Surrogate-assisted multi-objective genetic algorithms for fuzzy rule-based classification. Int. J. Fuzzy Syst. 20, 1938\u20131955 (2018). https:\/\/doi.org\/10.1007\/s40815-018-0478-3","journal-title":"Int. J. Fuzzy Syst."},{"issue":"2","key":"1392_CR70","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"1392_CR71","unstructured":"Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases. Ph.D. Thesis, Department of Information and Computer Science, University of California, Irvine, CA (1998)"}],"container-title":["International Journal of Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40815-022-01392-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40815-022-01392-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40815-022-01392-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T17:48:34Z","timestamp":1728323314000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40815-022-01392-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,8]]},"references-count":71,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["1392"],"URL":"https:\/\/doi.org\/10.1007\/s40815-022-01392-y","relation":{},"ISSN":["1562-2479","2199-3211"],"issn-type":[{"value":"1562-2479","type":"print"},{"value":"2199-3211","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,8]]},"assertion":[{"value":"8 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}