Affiliations: [a] Department of Computer Application, North Orissa University, Baripada, India | [b] Department of Information Technology, Institute of Engineering and Management, Salt Lake, Kolkata, India
Correspondence:
[*]
Corresponding author: Sujata Dash, Department of Computer Application, North Orissa University, Baripada, India. E-mail:[email protected]
Abstract: The microarray technology can exhibit
the expression levels of tens of thousands of genes simultaneously, which
helps to diagnose diseases particularly cancer at molecular level. But one
of the most challenging issues associated with this technology is the skewed
nature of the datasets, which makes the traditional classifiers inefficient
in producing accurate classification results. However, a lot of work
addressing this issue on binary class problems has been done by many
researchers. This paper has combined three different sampling techniques
namely, over sampling; under sampling and SMOTE with a meta-learning
algorithm `DECORATE' to deal with a highly imbalanced multi-class microarray
cancer dataset. The rate of accuracy of classification of the predictive
models in case of imbalanced problem cannot be considered as an appropriate
measure of effectiveness. Hence, different metrics are applied here to
measure the performance of the proposed hybrid methods of classification.
The experimental results show that unlike other traditional classification
algorithms, our proposed hybrid methods are not sensitive to highly skewed
multi-class microarray dataset.
Keywords: Imbalanced problem, SMOTE, under sampling, over sampling, resampling
