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Reseach Article

Learning from Small Data Set to Build Classification Model: A Survey

Published on May 2013 by Nidhi H. Ruparel, Nitin M. Shahane, Devyani P. Bhamare
International Conference on Recent Trends in Engineering and Technology 2013
Foundation of Computer Science USA
ICRTET - Number 4
May 2013
Authors: Nidhi H. Ruparel, Nitin M. Shahane, Devyani P. Bhamare
553a62b9-5612-4398-b4e1-8216de7079e4

Nidhi H. Ruparel, Nitin M. Shahane, Devyani P. Bhamare . Learning from Small Data Set to Build Classification Model: A Survey. International Conference on Recent Trends in Engineering and Technology 2013. ICRTET, 4 (May 2013), 23-26.

@article{
author = { Nidhi H. Ruparel, Nitin M. Shahane, Devyani P. Bhamare },
title = { Learning from Small Data Set to Build Classification Model: A Survey },
journal = { International Conference on Recent Trends in Engineering and Technology 2013 },
issue_date = { May 2013 },
volume = { ICRTET },
number = { 4 },
month = { May },
year = { 2013 },
issn = 0975-8887,
pages = { 23-26 },
numpages = 4,
url = { /proceedings/icrtet/number4/11787-1344/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Trends in Engineering and Technology 2013
%A Nidhi H. Ruparel
%A Nitin M. Shahane
%A Devyani P. Bhamare
%T Learning from Small Data Set to Build Classification Model: A Survey
%J International Conference on Recent Trends in Engineering and Technology 2013
%@ 0975-8887
%V ICRTET
%N 4
%P 23-26
%D 2013
%I International Journal of Computer Applications
Abstract

Classification is one of the important data mining techniques. Learning from a given data set to build a classification model becomes difficult when available sample size is small. How to extract more effective information from a small data set is thus of considerable interest. In this paper we provide a review of different classification methods which will help us build more amounts of data, so that classification performance is improved. We discuss different techniques which will work with small data set such as attribute construction, bootstrap method, incremental method and different diffusion functions. Different classification methods such as neural network, decision tree classifiers, Bayesian classifiers etc. are also discussed.

References
  1. Der-chiang Li and Chiao Wen Liu "Extending Attribute Information for Small Data Set Classification," IEEE Transactions On Knowledge And Data Engineering, Vol. 24, No. 3, March 2012.
  2. W. C. Li and C. W. Yeh, "A Non-Parametric Learning Algorithm for Small Manufacturing Data Sets," Expert Systems with Applications vol. 34, pp. 391-398, 2008.
  3. D. C. Li, C. S. Wu, T. I Tsai, and Y. S. Lina, "Using Mega-Trend-Diffusion and Artificial Samples in Small Data Set Learning for Early Flexible Manufacturing System Scheduling Knowledge," Computers and Operations Research, vol. 34, pp. 966-982, 2007.
  4. Kanthida Kusonmano, Michael Netzer, Bernhard Pfeifer, Christian Baumgartner, Klaus R. Liedl, and Armin Graber, "Evaluation of the Impact of Dataset Characteristics for Classification Problems in Biological Applications," World Academy of Science, Engineering and Technology 34 2009
  5. http://www. let. rug. nl/~tiedeman/ml05/03_bayesian_handout. pdf
  6. Guoqiang Peter Zhang "Neural Networks for Classification: A Survey," IEEE Transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 30, no. 4, November 2000
  7. Chongfu Huang, Claudio Moraga, "diffusion-neural-network for learning from small samples," International Journal of Approximate Reasoning 35 (2004) 137–161
  8. Yao San Lin, Der Chiang Li, "The Generalized Trend Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems"
  9. Tung-I Tsai, Der-Chiang Li , "Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems" Expert Systems with Applications 35 (2008) 1293–1300
Index Terms

Computer Science
Information Sciences

Keywords

Data Preprocessing Small Data Set Attribute Construction Smo