CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Meta-Learning with Landmarking: A Survey

by Ashvini Balte, Nitin Pise, Parag Kulkarni
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 8
Year of Publication: 2014
Authors: Ashvini Balte, Nitin Pise, Parag Kulkarni
10.5120/18401-9666

Ashvini Balte, Nitin Pise, Parag Kulkarni . Meta-Learning with Landmarking: A Survey. International Journal of Computer Applications. 105, 8 ( November 2014), 47-51. DOI=10.5120/18401-9666

@article{ 10.5120/18401-9666,
author = { Ashvini Balte, Nitin Pise, Parag Kulkarni },
title = { Meta-Learning with Landmarking: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 8 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 47-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number8/18401-9666/ },
doi = { 10.5120/18401-9666 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:13.367287+05:30
%A Ashvini Balte
%A Nitin Pise
%A Parag Kulkarni
%T Meta-Learning with Landmarking: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 8
%P 47-51
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Everyday large amount of data is generated. Data mining is a technique that extracts significant information from raw data using different classifiers. Meta-learning is machine learning technique that supports data mining for choosing appropriate classifier. Instead of whole data set, meta-learning extracts features from meta-data. So, it is important to identify the best suitable classifier based on meta-features. Landmarking is uncommon meta-feature extraction technique present in meta-learning. Instead of statistical measurement landmarking tries to determine position of training data in the areas of problem learning by directly measuring the performance of some simple and significant learning algorithms themselves. This paper is a survey of presented approach in landmarking area. A few research challenges are mentioned at the end of this paper.

References
  1. Vilalta, R, Giraud-carrier, C, Brazdil, P, Soares, C. 2004. Using meta-learning to support data mining, International Journal of Computer Science and Applications, 1(1), 31–45.
  2. Smith-Miles, K. A. 2008. Cross-disciplinary perspectives on meta-learning for algorithm selection", ACM Computing Surveys, 41(1), 1–25.
  3. Vilalta, R, Drissi, Y. 2002. A perspective view and survey of meta-learning, Journal of Artificial Intelligence Review, 18(2):77–95.
  4. Lemke, C, Budka, M, Gabrys, B. 2013. Metalearning: a survey of trends and technologies, Artificial Intelligence Review, Springer Netherlands1-14.
  5. Bhatt, N, Thakkar, A, Ganatra, A. 2012. A survey and current research challenges in meta-learning Approaches based on dataset characterisstic, International Journal of soft computing and Engineering, Volume-2, Issue-1.
  6. Brazdil, P, Soares, C, da Costa J. P. 2003. Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results, Machine Learning 50(3), 251-277.
  7. Ali, S, Smith, K. A. 2006. On learning algorithm selection for classification, Applied Soft Computing 6, 119-138.
  8. Peng, Y, Flach, P, Soares, C, Brazdil, P. 2002. Improved dataset characterization for meta-learning, S. Lange, K. Satoh, C. Smith (eds. ) Discovery Science, Lecture Notes in Computer Science, vol. 2534, pp. 193-208. Springer Berlin / Heidelberg.
  9. Bensusan, H. Giraud-Carrier, C. 2000. Casa batlo is in passeig de gracia or how landmark performances can describe tasks, Proceedings of the ECML-00 Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Com-bination, pp. 29-46.
  10. Pfahringer, B, Bensusan, H. Giraud-Carrier, C. 2000. Meta-learning by landmarking various learning algorithms, In Proceedings of the Seventeenth International Conference on Machine Learning, pp. 743-750. Morgan Kaufmann.
  11. Reif, M, Shafait, F, Goldstein, M, Breuel, T, Dengel, A. 2012. Automatic classifier selection for non-experts. Pattern Analysis and Applications pp. 1–14
  12. Abdelmessih, S. D, Shafait, F, Reif, M, Goldstein M. 2010. Landmarking for meta-learning using RapidMiner, RapidMiner Community Meeting and Conference.
  13. Bensusan, H, Giraud-Carrier, C. 2000. Discovering task neighbourhoods through land-mark learning performances, PKDD 00: Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 325-330. Springer-Verlag, London, UK.
  14. Furnkranz, J, Petrak, J. 2001 An evaluation of landmarking variants, In: C. Giraud-Carrier, N. Lavrac, S. Moyle, B. Kavsek (eds. ) Proceedings of the ECML/PKDD Workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning (IDDM-2001), pp. 57-68. Freiburg, Germany.
  15. Bensusan, H, Kalousis, A. 2001. Estimating the predictive accuracy of a classier, in: L. De Raedt, P. Flach (eds. ) Machine Learning: ECML 2001, Lecture Notes in Computer Science, vol. 2167, pp. 25-36. Springer Berlin / Heidelberg,
  16. Feurer, M, Springenberg, J. T, Hutter F. 2014. Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters, In: Proceedings 21st European Conference on Artificial intelligence 2014, workshop on Meta-Learning and Algorithm Selection pp. 3-10.
  17. Asuncion, A, Newman, D. 2007. D: UCI machine earninRepository http://www. ics. uci. edu/~mlearn/MLRepository. html University of California, Irvine, School of Information and Computer Sciences.
  18. Cacoveanu, S, Vidrighin C, Potolea R. 2009, Evolution Meta-Learning Framework For automatic Classifier Selection, IEEE 5th International Conference on Intelligent Computer Communation and Peocessing (ICCP).
  19. Ler, D, Koprinska, I, and Chawla, S. (2005). A hillclimbing landmarker generation algorithm based on efficiency and correlativity criteria. Proceedings of the 18th International FLAIRS Conference, Machine Learning Track.
  20. Vanwinckelen, G, Blockeel, H. 2014. A meta-learning system for multi-instance classification, in:Proceedings of the ECML-14 Workshop on Learning over Multiple Contexts, 1-14.
  21. Hall, M, Frank, E, Holmes, G,. Pfahringer, B, Reutemann P, Witten I. H. 2009. The WEKA Data Mining Software: An Update, SIGKDD Explorations, Volume 11, Issue 1.
  22. Mierswa, I, Wurst, M, Klinkenberg, R, Scholz, M, Euler, T, August 2006. Yale: Rapid prototyping for complex data mining tasks, In Lyle Ungar, Mark Craven, Dimitrios Gunopulos, and Tina Eliassi-Rad, editors, KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 935–940, New York, NY, USA.
  23. Thornton, C, Hutter, F, Hoos, H, 2013. Auto-WEKA Combined Selection and Hyperparameter Optimization of Classification Algorithm, KDD 2013 Chicago, Illinois, USA. ACM.
  24. Rossi, A, Carvalho, A, Soares, C, Souza, B, 2014. MetaStream: A Meta-learning Based Method for Periodic Algorithm Selection in Time Changing Data, Neurocomputing, 127: 52-64.
Index Terms

Computer Science
Information Sciences

Keywords

Meta-learning Landmarking Algorithm selection Classifier Accuracy

Powered by PhDFocusTM