Academic literature on the topic 'Prediction Explanation'
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Journal articles on the topic "Prediction Explanation"
Pintelas, Emmanuel, Meletis Liaskos, Ioannis E. Livieris, Sotiris Kotsiantis, and Panagiotis Pintelas. "Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction." Journal of Imaging 6, no. 6 (May 28, 2020): 37. http://dx.doi.org/10.3390/jimaging6060037.
Full textHalliwell, Nicholas. "Evaluating Explanations of Relational Graph Convolutional Network Link Predictions on Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12880–81. http://dx.doi.org/10.1609/aaai.v36i11.21577.
Full textHalliwell, Nicholas, Fabien Gandon, and Freddy Lecue. "A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction Using Relational Graph Convolutional Networks (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12963–64. http://dx.doi.org/10.1609/aaai.v36i11.21618.
Full textEL Shawi, Radwa, and Mouaz H. Al-Mallah. "Interpretable Local Concept-based Explanation with Human Feedback to Predict All-cause Mortality." Journal of Artificial Intelligence Research 75 (November 18, 2022): 833–55. http://dx.doi.org/10.1613/jair.1.14019.
Full textBonacich, Phillip. "EXPLANATION AND PREDICTION." Rationality and Society 9, no. 3 (August 1997): 373–77. http://dx.doi.org/10.1177/104346397009003006.
Full textSøgaard, Villy. "Explanation versus prediction." Technological Forecasting and Social Change 43, no. 2 (March 1993): 201–2. http://dx.doi.org/10.1016/0040-1625(93)90018-3.
Full textBartsch, Karen. "False Belief Prediction and Explanation: Which Develops First and Why it Matters." International Journal of Behavioral Development 22, no. 2 (June 1998): 423–28. http://dx.doi.org/10.1080/016502598384450.
Full textPlomin, Robert, and Sophie von Stumm. "Polygenic scores: prediction versus explanation." Molecular Psychiatry 27, no. 1 (October 22, 2021): 49–52. http://dx.doi.org/10.1038/s41380-021-01348-y.
Full textDouglas, Heather E. "Reintroducing Prediction to Explanation." Philosophy of Science 76, no. 4 (October 2009): 444–63. http://dx.doi.org/10.1086/648111.
Full textKishimoto, T., and T. Sato. "+: Another Explanation and Prediction." Progress of Theoretical Physics 116, no. 1 (July 1, 2006): 241–46. http://dx.doi.org/10.1143/ptp.116.241.
Full textDissertations / Theses on the topic "Prediction Explanation"
Gordon, Richard Douglas. "Explanation and prediction in the labour process theory." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/30583.
Full textArts, Faculty of
Philosophy, Department of
Graduate
Bonawitz, Elizabeth R. (Elizabeth Robbin). "The rational child : theories and evidence in prediction, exploration, and explanation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/47891.
Full textIncludes bibliographical references (p. 122-133).
In this thesis, rational Bayesian models and the Theory-theory are bridged to explore ways in which children can be described as Bayesian scientists. I investigate what it means for children to take a rational approach to processes that support learning. In particular, I present empirical studies that show children making rational predictions, exploration, and explanations. I test the claim that differences in prior beliefs or changes in the observed evidence should affect these behaviors. The studies presented in this thesis encompass two manipulations: in some conditions, children's prior beliefs are equal, but the patterns of evidence are varied; in other conditions, children observe identical evidence but children's prior beliefs are varied. I incorporate an additional approach in this thesis, testing children within a variety of domains, tapping into their intuitive theories of biological kinds, psychosomatic illness, balance, and physical systems. Chapter One introduces the problem. Chapter Two explores how evidence and children's strong beliefs about biological events and psychosomatic illness influence their forced-choice explanations in a story-book task. Chapter Three presents a training study to further investigate the developmental differences discussed in Chapter Two. Chapter Four looks at how children's strong differential beliefs of balance interact with evidence to affect their predictions, play, explanations, and learning.
(cont.) Chapter Five looks at children's exploratory play with a jack-in-the-box, (where children don't have strong, differential beliefs), given different patterns of evidence. Chapter Six investigates children's explanations following theory-neutral evidence about a mechanical toy. Chapter Seven concludes the thesis. The following chapters will suggest that frameworks combining evidence and theories capture children's causal learning about the world.
by Elizabeth R. Bonawitz.
Ph.D.
Watson, Jason Paul 1971. "Explanation and prediction of curious experimental phenomena in lasers and nonlinear optics." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/282875.
Full textHaar, D. H. "Formalised modelling of action theory in the explanation of crime for prediction, deduction and intervention." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599815.
Full textMcKay, William L. "Hope and suicide resilience in the prediction and explanation of suicidality experiences in university students." Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1456285751&sid=3&Fmt=2&clientId=18949&RQT=309&VName=PQD.
Full textOlofsson, Nina. "A Machine Learning Ensemble Approach to Churn Prediction : Developing and Comparing Local Explanation Models on Top of a Black-Box Classifier." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210565.
Full textMetoder för att prediktera utträde är vanliga inom Customer Relationship Management och har visat sig vara värdefulla när det kommer till att behålla kunder. För att kunna prediktera utträde med så hög säkerhet som möjligt har den senasteforskningen fokuserat på alltmer komplexa maskininlärningsmodeller, såsom ensembler och hybridmodeller. En konsekvens av att ha alltmer komplexa modellerär dock att det blir svårare och svårare att förstå hur en viss modell har kommitfram till ett visst beslut. Tidigare studier inom maskininlärningsinterpretering har haft ett globalt perspektiv för att förklara svårförståeliga modeller. Denna studieutforskar lokala förklaringsmodeller för att förklara individuella beslut av en ensemblemodell känd som 'Random Forest'. Prediktionen av utträde studeras påanvändarna av Tink – en finansapp. Syftet med denna studie är att ta lokala förklaringsmodeller ett steg längre genomatt göra jämförelser av indikatorer för utträde mellan olika användargrupper. Totalt undersöktes tre par av grupper som påvisade skillnader i tre olika variabler. Sedan användes lokala förklaringsmodeller till att beräkna hur viktiga alla globaltfunna indikatorer för utträde var för respektive grupp. Resultaten visade att detinte fanns några signifikanta skillnader mellan grupperna gällande huvudindikatorerna för utträde. Istället visade resultaten skillnader i mindre viktiga indikatorer som hade att göra med den typ av information som lagras av användarna i appen. Förutom att undersöka skillnader i indikatorer för utträde resulterade dennastudie i en välfungerande modell för att prediktera utträde med förmågan attförklara individuella beslut. Random Forest-modellen visade sig vara signifikantbättre än ett antal enklare modeller, med ett AUC-värde på 0.93.
Hasan, Rakebul. "Prédire les performances des requêtes et expliquer les résultats pour assister la consommation de données liées." Thesis, Nice, 2014. http://www.theses.fr/2014NICE4082/document.
Full textOur goal is to assist users in understanding SPARQL query performance, query results, and derivations on Linked Data. To help users in understanding query performance, we provide query performance predictions based on the query execution history. We present a machine learning approach to predict query performances. We do not use statistics about the underlying data for our predictions. This makes our approach suitable for the Linked Data scenario where statistics about the underlying data is often missing such as when the data is controlled by external parties. To help users in understanding query results, we provide provenance-based query result explanations. We present a non-annotation-based approach to generate why-provenance for SPARQL query results. Our approach does not require any re-engineering of the query processor, the data model, or the query language. We use the existing SPARQL 1.1 constructs to generate provenance by querying the data. This makes our approach suitable for Linked Data. We also present a user study to examine the impact of query result explanations. Finally to help users in understanding derivations on Linked Data, we introduce the concept of Linked Explanations. We publish explanation metadata as Linked Data. This allows explaining derived data in Linked Data by following the links of the data used in the derivation and the links of their explanation metadata. We present an extension of the W3C PROV ontology to describe explanation metadata. We also present an approach to summarize these explanations to help users filter information in the explanation, and have an understanding of what important information was used in the derivation
Rawstorne, Patrick. "A systematic analysis of the theory of reasoned action, the theory of planned behaviour and the technology acceptance model when applied to the prediction and explanation of information systems use in mandatory usage contexts." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060815.154410/index.html.
Full textBantegnie, Brice. "Eliminating propositional attitudes concepts." Thesis, Paris, Ecole normale supérieure, 2015. http://www.theses.fr/2015ENSU0020.
Full textIn this dissertation, I argue for the elimination of propositional attitudes concepts. In the first chapter I sketch the landscape of eliminativism in contemporary philosophy of mind and cognitive science. There are two kinds of eliminativism: eliminative materialism and concept eliminativism. One can further distinguish between folk and science eliminativism about concepts: whereas the former says that the concept should be eliminated from our folk theories, the latter says that the concept should be eliminated form our scientific theories. The eliminativism about propositional attitudes concepts I defend is a species of the latter. In the next three chapters I put forward three arguments for this thesis. I first argue that the interventionist theory of causation cannot lend credit to our claims of mental causation. I then support the thesis by showing that propositional attitudes concepts aren't natural kind concepts because they cross-cut the states of the modules posited by the thesis of massive modularity, a thesis which, I contend, is part of our best research-program. Finally, my third argument rests on science eliminativism about the concept of mental content. In the two last chapters of the dissertation I first defend the elimination of the concept of mental content from the success argument, according to which as psychologists produce successful science while using the concept of mental content, the concept should be conserved. Then, I dismiss an alternative way of eliminating the concept, that is, the way taken by proponents of extended cognition, by refuting what I take to be the best argument for extended cognition, namely, the system argument
Lonjarret, Corentin. "Sequential recommendation and explanations." Thesis, Lyon, 2021. http://theses.insa-lyon.fr/publication/2021LYSEI003/these.pdf.
Full textRecommender systems have received a lot of attention over the past decades with the proposal of many models that take advantage of the most advanced models of Deep Learning and Machine Learning. With the automation of the collect of user actions such as purchasing of items, watching movies, clicking on hyperlinks, the data available for recommender systems is becoming more and more abundant. These data, called implicit feedback, keeps the sequential order of actions. It is in this context that sequence-aware recommender systems have emerged. Their goal is to combine user preference (long-term users' profiles) and sequential dynamics (short-term tendencies) in order to recommend next actions to a user. In this thesis, we investigate sequential recommendation that aims to predict the user's next item/action from implicit feedback. Our main contribution is REBUS, a new metric embedding model, where only items are projected to integrate and unify user preferences and sequential dynamics. To capture sequential dynamics, REBUS uses frequent sequences in order to provide personalized order Markov chains. We have carried out extensive experiments and demonstrate that our method outperforms state-of-the-art models, especially on sparse datasets. Moreover we share our experience on the implementation and the integration of REBUS in myCADservices, a collaborative platform of the French company Visiativ. We also propose methods to explain the recommendations provided by recommender systems in the research line of explainable AI that has received a lot of attention recently. Despite the ubiquity of recommender systems only few researchers have attempted to explain the recommendations according to user input. However, being able to explain a recommendation would help increase the confidence that a user can have in a recommendation system. Hence, we propose a method based on subgroup discovery that provides interpretable explanations of a recommendation for models that use implicit feedback
Books on the topic "Prediction Explanation"
Dieks, Dennis, Wenceslao J. Gonzalez, Stephan Hartmann, Thomas Uebel, and Marcel Weber, eds. Explanation, Prediction, and Confirmation. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8.
Full textDennis Geert Bernardus Johan Dieks. Explanation, Prediction, and Confirmation. Dordrecht: Springer Science+Business Media B.V., 2011.
Find full textP, Farrington David, ed. Criminal recidivism: Explanation, prediction and prevention. Abingdon, Oxon: Routledge, 2015.
Find full textBonneson, James, and John Ivan. Theory, Explanation, and Prediction in Road Safety. Washington, D.C.: Transportation Research Board, 2013. http://dx.doi.org/10.17226/22465.
Full textMultiple regression in behavioral research: Explanation and prediction. 3rd ed. Forth Worth: Harcourt Brace College Publishers, 1997.
Find full textL, Casti J., Karlqvist Anders, and Sweden Forskningsrådsnämnden, eds. Beyond belief: Randomness, prediction, and explanation in science. Boca Raton, Fla: CRC Press, 1991.
Find full textEriksson, Bo G. Studying ageing: Experiences, description, variation, prediction and explanation. Göteborg: Department of Sociology, University of Gothenburg, 2010.
Find full textEriksson, Bo G., and Bo G. Eriksson. Studying ageing: Experiences, description, variation, prediction and explanation. Göteborg: Department of Sociology, University of Gothenburg, 2010.
Find full textTrasler, Gordon. The Explanation of criminality. London: Routledge & Kegan Paul, 1998.
Find full textRastogi, P. N. Ethnic tensions in Indian society: Explanation, prediction, monitoring, and control. Delhi, India: Mittal Publications, 1986.
Find full textBook chapters on the topic "Prediction Explanation"
Worrall, John. "The No Miracles Intuition and the No Miracles Argument." In Explanation, Prediction, and Confirmation, 11–21. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_1.
Full textReutlinger, Alexander. "What’s Wrong with the Pragmatic-Ontic Account of Mechanistic Explanation?" In Explanation, Prediction, and Confirmation, 141–52. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_10.
Full textJoffe, Michael. "Causality and Evidence Discovery in Epidemiology." In Explanation, Prediction, and Confirmation, 153–66. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_11.
Full textGraßhoff, Gerd. "Inferences to Causal Relevance from Experiments." In Explanation, Prediction, and Confirmation, 167–82. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_12.
Full textLove, Alan C., and Andreas Hüttemann. "Comparing Part-Whole Reductive Explanations in Biology and Physics1." In Explanation, Prediction, and Confirmation, 183–202. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_13.
Full textMcLaughlin, Peter. "The Arrival of the Fittest." In Explanation, Prediction, and Confirmation, 203–22. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_14.
Full textReydon, Thomas A. C. "The Arrival of the Fittest What?" In Explanation, Prediction, and Confirmation, 223–37. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_15.
Full textSpohn, Wolfgang. "Normativity is the Key to the Difference Between the Human and the Natural Sciences." In Explanation, Prediction, and Confirmation, 241–51. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_16.
Full textLenk, Hans. "Methodological Higher-Level Interdisciplinarity by Scheme-Interpretationism: Against Methodological Separatism of the Natural, Social, and Human Sciences." In Explanation, Prediction, and Confirmation, 253–67. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_17.
Full textFaye, Jan. "Explanation and Interpretation in the Sciences of Man." In Explanation, Prediction, and Confirmation, 269–79. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1180-8_18.
Full textConference papers on the topic "Prediction Explanation"
Kim, Marie, Jong-Arm Jun, YuJin Song, and Cheol Sig Pyo. "Explanation for building energy prediction." In 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2020. http://dx.doi.org/10.1109/ictc49870.2020.9289340.
Full textAndric, Marina, Iustina Ivanova, and Francesco Ricci. "Climbing Route Difficulty Grade Prediction and Explanation." In WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3486622.3493932.
Full textLi, Liangyue, and Hanghang Tong. "Uncovering Teamwork in Networks — Prediction, Optimization and Explanation." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.160.
Full textZhang Bofeng and Liu Yue. "Customized explanation in expert system for earthquake prediction." In 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05). IEEE, 2005. http://dx.doi.org/10.1109/ictai.2005.54.
Full textLa Malfa, Emanuele, Rhiannon Michelmore, Agnieszka M. Zbrzezny, Nicola Paoletti, and Marta Kwiatkowska. "On Guaranteed Optimal Robust Explanations for NLP Models." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/366.
Full textPereira, Filipe Dwan, Elaine Harada Teixeira de Oliveira, David Braga Fernandes de Oliveira, Leandro Silva Galvão de Carvalho, and Alexandra Ioana Cristea. "Interpretable AI to Understand Early Effective and Ineffective Programming Behaviours from CS1 Learners." In Anais Estendidos do Simpósio Brasileiro de Educação em Computação. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/educomp_estendido.2021.14853.
Full textZhang, Wen, Bibek Paudel, Wei Zhang, Abraham Bernstein, and Huajun Chen. "Interaction Embeddings for Prediction and Explanation in Knowledge Graphs." In WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3289600.3291014.
Full textTreviso, Marcos, and André F. T. Martins. "The Explanation Game: Towards Prediction Explainability through Sparse Communication." In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.blackboxnlp-1.10.
Full textLi, Yi, and Guo-en Xia. "The Explanation of Support Vector Machine in Customer Churn Prediction." In 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5660501.
Full textBanerjee, Bonny, and Jayanta K. Dutta. "Efficient learning from explanation of prediction errors in streaming data." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691728.
Full textReports on the topic "Prediction Explanation"
Lalisse, Matthias. Measuring the Impact of Campaign Finance on Congressional Voting: A Machine Learning Approach. Institute for New Economic Thinking Working Paper Series, February 2022. http://dx.doi.org/10.36687/inetwp178.
Full textFridman, Eyal, Jianming Yu, and Rivka Elbaum. Combining diversity within Sorghum bicolor for genomic and fine mapping of intra-allelic interactions underlying heterosis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597925.bard.
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