Genetic Algorithm as Machine Learning for Profiles Recognition

Yann Carbonne, Christelle Jacob

2015

Abstract

Persons are often asked to provide information about themselves. These data are very heterogeneous and result in as many “profiles” as contexts. Sorting a large amount of profiles from different contexts and assigning them back to a specific individual is quite a difficult problem. Semantic processing and machine learning are key tools to achieve this goal. This paper describes a framework to address this issue by means of concepts and algorithms selected from different Artificial Intelligence fields. Indeed, a Vector Space Model is customized to first transpose semantic information into a mathematical model. Then, this model goes through a Genetic Algorithm (GA) which is used as a supervised learning algorithm for training a computer to determine how much two profiles are similar. Amongst the GAs, this study introduces a new reproduction method (Best Together), and compare it to some usual ones (Wheel, Binary Tournament).This paper also evaluates the accuracy of the GAs predictions for profiles clustering with the computation of a similarity score, as well as its ability to classify two profiles are similar or non-similar. We believe that the overall methodology can be used for any kind of sources using profiles and, more generally, for similar data recognition. 1

References

  1. A. K. Jain, M. M. P. F., 1999. Data Clustering: A Review. ACM Computing Surveys (CSUR), Volume 31, pp. 264- 323.
  2. Campbell, G. & Zweig, M. H., 1993. Receiver-Operation Characteristic (ROC) Plots: A Fundamental Evaluation Tool in Clinical Medicine. Clin Chem, 39(4), pp. 561- 577.
  3. Dasarathy, B. V., 1991. Neared neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press.
  4. Eberhart, R. C., Simpson, P. K. & Dobbins, R., 1996. Computational Intelligence PC Tools. s.l.:AP Professional.
  5. Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
  6. He, H. & Garcia, E. A., 2009. Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, Volume 21, pp. 1263-1284.
  7. Holland, J. H., 1992. Adaptation in natural and artificial systems. Cambridge, MA, USA: MIT Press.
  8. Hüe, X., 1997. Genetic Algorithms for Optimisation : Background and Applications. s.l.:s.n.
  9. Kim, H. & Cho, S., 2000. Application of interactive genetic algorithm to fashion design. Engineering Applications of ArtiÆcial Intelligence.
  10. Kluwer Academic Publishers, 2001. Genetic Algorithms and Machine Learning. Machine Learning.
  11. Miller, B. L. & Goldberg, D. E., 1995. Genetic Algorithms, Tournament Selection, and the Effects of Noise. Complex Systems, Issue 9, pp. 193-212.
  12. Moorhead, P. S. & Kaplan, M. M., 1967. Mathematical challenges to the neo-Darwinian interpretation of evolution. Wistar institute symposium monograph, Issue 5.
  13. Rawashdeh, A. & Ralescu, A. L., 2014. Similarity Measure for Social Networks - A Brief Survey.
  14. Resende, M. G., 2010. Biased Random-key genetic algorithms with applications in telecommunications. AT&T Labs Research Technical Report.
  15. Salton, G., 1968. Automatic Information Organization and Retrieval.
  16. Shen, Y., 2005. Loss Functions for Binary Classification and Class Probability Estimation, s.l.: University of Pennsylvania.
  17. Srinivas, M. & Patnaik, L. M., 1994. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on systems, man and cybernetics, Volume 24, pp. 656-667.
  18. Turney, P. D. & Pantel, P., 2010. From Frequency to Meaning : Vector Space Models of Semantics. Journal of Artificial Intelligence Research.
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Paper Citation


in Harvard Style

Carbonne Y. and Jacob C. (2015). Genetic Algorithm as Machine Learning for Profiles Recognition . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 157-166. DOI: 10.5220/0005590501570166


in Bibtex Style

@conference{ecta15,
author={Yann Carbonne and Christelle Jacob},
title={Genetic Algorithm as Machine Learning for Profiles Recognition},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={157-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005590501570166},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Genetic Algorithm as Machine Learning for Profiles Recognition
SN - 978-989-758-157-1
AU - Carbonne Y.
AU - Jacob C.
PY - 2015
SP - 157
EP - 166
DO - 10.5220/0005590501570166