Sampling correctly for improving classification accuracy: a hybrid higher order neural classifier (HHONC) approach

  • Authors:

  • Prem Pujari Pati;Kaberi Das;Debahuti Mishra;Shruti Mishra;Lipismita Panigrahi

  • Affiliations:

  • ITER, Siksha O Anusandhan University, Bhubaneswar, Odisha, India;ITER, Siksha O Anusandhan University, Bhubaneswar, Odisha, India;ITER, Siksha O Anusandhan University, Bhubaneswar, Odisha, India;ITER, Siksha O Anusandhan University, Bhubaneswar, Odisha, India;ITER, Siksha O Anusandhan University, Bhubaneswar, Odisha, India

  • Venue:
  • Proceedings of the International Conference on Advances in Computing, Communications and Informatics
  • Year:
  • 2012

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Abstract

Data sets contain very large amount of information, which is not an easy task for the users to scan the entire data set. The researcher's initial task is to formulate a realistic explanation for the use of sampling in his research. Sampling has been often suggested as an effective tool to reduce the size of the dataset operated at some cost to accuracy. It is the the process of selecting a representative part of a data set for the purpose of determining parameters or characteristics of the whole data set. Due to sampling we overcome the problems like; i) in research it is not possible to collect and test each and every element from the data base individually; and ii) study of sample rather than the entire dataset is also sometimes likely to produce more reliable results. This paper focuses on different types of sampling strategies applied on hybrid higher order neural network classifier (HHONC) rather than artificial neural network which is having several limitations. To overcome such limitations HHONC have been used. Here sampling technique has been applied on four real, integers and categorical dataset such as breast cancer, pima Indian diabetes, leukaemia and lung cancer data set prior to classification. The main objective of this paper is an empirical comparison of different sampling strategies for classification which gives more accuracy.