Spatial relation learning for explainable image classification and annotation in critical applications
Tài liệu tham khảo
Miller, 2019, Explanation in artificial intelligence: insights from the social sciences, Artif. Intell., 267, 1, 10.1016/j.artint.2018.07.007
Rudin, 2019, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell., 1, 206, 10.1038/s42256-019-0048-x
Ribeiro, 2016, Why should I trust you?: Explaining the predictions of any classifier, 1135
Lundberg, 2017, A unified approach to interpreting model predictions, vol. 30, 4765
Simonyan
Alvarez Melis, 2018, Towards robust interpretability with self-explaining neural networks, vol. 31, 7775
Biederman, 1981
Geurts, 2001, Pattern extraction for time series classification, 115
Holzinger, 2016, Interactive machine learning for health informatics: when do we need the human-in-the-loop?, Brain Inform., 3, 119, 10.1007/s40708-016-0042-6
Holzinger, 2019, Causability and explainability of artificial intelligence in medicine, Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 9, 10.1002/widm.1312
González, 2012, An efficient inductive genetic learning algorithm for fuzzy relational rules, Int. J. Comput. Intell. Syst., 5, 212, 10.1080/18756891.2012.685265
Feigenbaum, 1970
Lindsay, 1993, Dendral: a case study of the first expert system for scientific hypothesis formation, Artif. Intell., 61, 209, 10.1016/0004-3702(93)90068-M
Shortliffe, 1975, A model of inexact reasoning in medicine, Math. Biosci., 23, 351, 10.1016/0025-5564(75)90047-4
Letham, 2015, Interpretable classifiers using rules and bayesian analysis: building a better stroke prediction model, Ann. Appl. Stat., 9, 1350, 10.1214/15-AOAS848
Lakkaraju, 2016, Interpretable decision sets: a joint framework for description and prediction, 1675
Malioutov, 2017, Learning interpretable classification rules with boolean compressed sensing, 95
Nelder, 1972, Generalized linear models, J. R. Stat. Soc. A, 135, 370, 10.2307/2344614
Hastie, 1986, Generalized additive models, Stat. Sci., 297
An evaluation of the human-interpretability of explanation.
Poursabzi-Sangdeh
Kurakin
Chen, 2018, Learning to explain: an information-theoretic perspective on model interpretation, vol. 80, 883
Zeiler, 2014, Visualizing and understanding convolutional networks, 818
Springenberg
Montavon, 2017, Explaining nonlinear classification decisions with deep Taylor decomposition, Pattern Recognit., 65, 211, 10.1016/j.patcog.2016.11.008
Kindermans
Sundararajan, 2017, Axiomatic attribution for deep networks, 3319
Erhan, 2009
Yosinski
Olah
Craven, 1996, Extracting tree-structured representations of trained networks, 24
Buciluǎ, 2006, Model compression, 535
Hinton
Frosst
Kim, 2018, Interpretability beyond feature attribution: quantitative testing with concept activation vectors (tcav), 2673
Graziani, 2018, Regression concept vectors for bidirectional explanations in histopathology, 124
Ghorbani, 2019, Towards automatic concept-based explanations, 9273
Kim, 2016, Examples are not enough, learn to criticize! Criticism for interpretability, vol. 29, 2280
Zadeh, 1965, Fuzzy sets, Inf. Control, 8, 338, 10.1016/S0019-9958(65)90241-X
Zadeh, 1975, The concept of a linguistic variable and its application to approximate reasoning—I, Inf. Sci., 8, 199, 10.1016/0020-0255(75)90036-5
Baczynski, 2015, 183
Agrawal, 1993, Mining association rules between sets of items in large databases, 207
Pierrard, 2018, A fuzzy close algorithm for mining fuzzy association rules, 88
Montanari, 1974, Networks of constraints: fundamental properties and applications to picture processing, Inf. Sci., 7, 95, 10.1016/0020-0255(74)90008-5
Mackworth, 1977, Consistency in networks of relations, Artif. Intell., 8, 99, 10.1016/0004-3702(77)90007-8
Waltz, 1975, Understanding line drawings of scenes with shadows
Dubois, 1996, Possibility theory in constraint satisfaction problems: handling priority, preference and uncertainty, Appl. Intell., 6, 287, 10.1007/BF00132735
Vanegas, 2016, Fuzzy constraint satisfaction problem for model-based image interpretation, Fuzzy Sets Syst., 286, 1, 10.1016/j.fss.2014.10.025
Byrne, 2009, ‘If’ and the problems of conditional reasoning, Trends Cogn. Sci., 13, 282, 10.1016/j.tics.2009.04.003
Pedreschi, 2019, Meaningful explanations of black box AI decision systems, vol. 33, 9780
Turney, 1995, Bias and the quantification of stability, Mach. Learn., 20, 23, 10.1007/BF00993473
Dwyer, 2007, Decision tree instability and active learning, 128
Zadeh, 1999, Fuzzy logic = computing with words, 3
Bloch, 2005, Fuzzy spatial relationships for image processing and interpretation: a review, Image Vis. Comput., 23, 89, 10.1016/j.imavis.2004.06.013
Levesque, 1987, Expressiveness and tractability in knowledge representation and reasoning 1, Comput. Intell., 3, 78, 10.1111/j.1467-8640.1987.tb00176.x
Hendricks, 2016, Generating visual explanations, 3
Lesot, 2008, Fuzzy prototypes: from a cognitive view to a machine learning principle, 431
Kellogg, 1980, Feature frequency and hypothesis testing in the acquisition of rule-governed concepts, Mem. Cogn., 8, 297, 10.3758/BF03197618
Au, 1998, An effective algorithm for discovering fuzzy rules in relational databases, vol. 2, 1314
Kuok, 1998, Mining fuzzy association rules in databases, SIGMOD Rec., 27, 41, 10.1145/273244.273257
Hong, 1999, Mining association rules from quantitative data, Intell. Data Anal., 3, 363
Hong, 2004, A fuzzy aprioriTid mining algorithm with reduced computational time, Appl. Soft Comput., 5, 1, 10.1016/j.asoc.2004.03.009
Papadimitriou, 2005, The fuzzy frequent pattern tree, 1
Lin, 2010, An efficient tree-based fuzzy data mining approach, Int. J. Fuzzy Syst., 12, 150
Lin, 2010, A two-phase fuzzy mining approach, 1
Belohlavek, 2012
Montes, 2015, 171
Yager, 1991, The representation of fuzzy relational production rules, Appl. Intell., 1, 35, 10.1007/BF00117744
Yager, 1996, Relational partitioning of fuzzy rules, Fuzzy Sets Syst., 80, 57, 10.1016/0165-0114(95)00131-X
Pasquier, 1999, Efficient mining of association rules using closed itemset lattices, Inf. Sci., 24, 25
Fodor, 2015, 159
Magdalena, 2015, 203
Gatt, 2009, Simplenlg: a realisation engine for practical applications, 90
Baaj, 2019, Natural language generation of explanations of fuzzy inference decisions, 1
Bloch, 1999, Fuzzy relative position between objects in image processing: a morphological approach, IEEE Trans. Pattern Anal. Mach. Intell., 21, 657, 10.1109/34.777378
Pierrard, 2020, Simd-based exact parallel fuzzy dilation operator for fast computing of fuzzy spatial relations, 1
Cayrol, 1982, Fuzzy pattern matching, Kybernetes, 11, 103, 10.1108/eb005612
Chanussot, 2005, Shape signatures of fuzzy star-shaped sets based on distance from the centroid, Pattern Recognit. Lett., 26, 735, 10.1016/j.patrec.2004.09.025
Kahn, 1962, Topological sorting of large networks, Commun. ACM, 5, 558, 10.1145/368996.369025
Jimenez-del Toro, 2016, Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: visceral anatomy benchmarks, IEEE Trans. Med. Imaging, 35, 2459, 10.1109/TMI.2016.2578680
Tuzikov, 2003, Evaluation of the symmetry plane in 3d mr brain images, Pattern Recognit. Lett., 24, 2219, 10.1016/S0167-8655(03)00049-7
Cawley, 2010, On over-fitting in model selection and subsequent selection bias in performance evaluation, J. Mach. Learn. Res., 11, 2079
Doshi-Velez
R. Likert, A technique for the measurement of attitudes, Archives of psychology.