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International Journal for Research Trends and Innovation
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Authors Name: Mr.R.Bulli Babu , Dr.Mohammed Ali Hussain
Unique Id: IJRTI1704043
Published In: Volume 2 Issue 4, April-2017
Abstract: Fraud is a hugely costly criminal activity which occurs in a number of domains. Data mining has been applied to fraud detection in both a supervised and non-supervised manner. When a supervised data mining approach is used, one of the biggest problems that are encountered, is the problem of class imbalance. The class imbalance problem is not unique to the domain of fraud, but also occurs in fields as diverse as medical diagnosis and quality control. There are two basic means of overcoming the class imbalance problem; these are data methods and algorithmic methods. Data methods generally involve an under sampling, over sampling or hybrid over/under sampling approach. Other data method investigated include SMOTE, which uses a K-NN learner to artificially synthesize minority class samples. Algorithmic methods investigated include using either a mis-classification cost in the case of the Meta cost procedure or Meta cost thresholds. Other algorithmic methods include the use of learners which are not sensitive to the class imbalance problem. The different methods for overcoming the class imbalance problem are implemented using open-source software. 3 datasets are used to investigate the usefulness of the different methods. 2 of the datasets are from the domain of fraud, while the third is from the domain of medical diagnosis.
Keywords: Dataset, class imbalance, under-sampling, over-sampling, Class Imbalance Learning
Cite Article: "IMPROVEMENT OF CLASS IMBALANCE LEARNING TECHNIQUES USING UNSUPERVISED LEARNING APPROACHES", International Journal of Science & Engineering Development Research (, ISSN:2455-2631, Vol.2, Issue 4, page no.174 - 182, April-2017, Available :
Downloads: 000968
Publication Details: Published Paper ID: IJRTI1704043
Registration ID:170160
Published In: Volume 2 Issue 4, April-2017
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ISSN Number: 2456 - 3315
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