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Inference of Markov Chain Models by Using k-Testable Language: Application on Aging People

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Transactions on Computational Collective Intelligence XVII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 8790))

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Abstract

We investigate the contribution of unsupervised learning and regular grammatical inference to respectively identify profiles of elderly people and their development over time in order to evaluate care needs (human, financial and physical resources). Grammatical Inference (also known as automata induction, grammar induction and automatic language acquisition) allows grammar and language learning from data. Machine learning by using grammar has a variety of applications: pattern recognition, adaptive intelligent agents, diagnosis, biology, systems modelling, prediction, natural language acquisition, data mining… The proposed approach is based on regular grammar. An adaptation of k-Testable Languages in the Strict Sense Inference algorithm is proposed in order to infer a probabilistic automaton from which a Markovian model which has a discrete (finite or countable) state-space has been deduced. In simulating the corresponding Markov chain model, it is possible to obtain information on population ageing. We have verified if our observed system conforms to a unique long term state vector, called the stationary distribution and the steady-state.

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Acknowledgements

The authors would like to acknowledge Mr. F. Navarro (Chairman of the Board of “Mutualité Française” Rhône-Alpes - France), as well as all the staff who had the kind enough to entrust us this project, data to validate our models and who answered our numerous questions. The authors are very grateful to the reviewers for their comments which were both useful and helpful.

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Authors and Affiliations

  1. University of Lyon, Hubert CURIEN Laboratory, UMR CNRS 5516, University of Jean Monnet, 18 rue Benoît Lauras, 42023, Saint-Etienne Cedex 2, France

    Catherine Combes

  2. University of Jean Monnet, 23 avenue du Docteur Paul Michelon, 42023, Saint-Etienne Cedex 2, France

    Jean Azema

Authors
  1. Catherine Combes
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  2. Jean Azema
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Correspondence to Catherine Combes .

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Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Melbourne, Australia and Polish Academy of Sciences, Warsaw, Poland

    Ryszard Kowalczyk

  3. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  4. Polytechnic Institute of SĂ©tubal, SĂ©tubal, Portugal

    Filipe Joaquim

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Combes, C., Azema, J. (2014). Inference of Markov Chain Models by Using k-Testable Language: Application on Aging People. In: Nguyen, N., Kowalczyk, R., Fred, A., Joaquim, F. (eds) Transactions on Computational Collective Intelligence XVII. Lecture Notes in Computer Science(), vol 8790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44994-3_5

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  • DOI: https://doi.org/10.1007/978-3-662-44994-3_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44993-6

  • Online ISBN: 978-3-662-44994-3

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