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A Novel Approach for Handling Imbalanced Data in Medical Diagnosis using Undersampling Technique

Varsha Babar, Roshani Ade. Published in Biomedical.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Varsha Babar, Roshani Ade

Varsha Babar and Roshani Ade. A Novel Approach for Handling Imbalanced Data in Medical Diagnosis using Undersampling Technique. Communications on Applied Electronics 5(7):36-42, July 2016. BibTeX

	author = {Varsha Babar and Roshani Ade},
	title = {A Novel Approach for Handling Imbalanced Data in Medical Diagnosis using Undersampling Technique},
	journal = {Communications on Applied Electronics},
	issue_date = {July 2016},
	volume = {5},
	number = {7},
	month = {Jul},
	year = {2016},
	issn = {2394-4714},
	pages = {36-42},
	numpages = {7},
	url = {},
	doi = {10.5120/cae2016652323},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In many data mining applications the imbalanced learning problem is becoming ubiquitous nowadays. When the data sets have an unequal distribution of samples among classes, then these data sets are known as imbalanced data sets. When such highly imbalanced data sets are given to any classifier, then classifier may misclassify the rare samples from the minority class. To deal with such type of imbalance, several undersampling as well as oversampling methods were proposed. Many undersampling techniques do not consider distribution of information among the classes, similarly some oversampling techniques lead to the overfitting or may cause overgeneralization problem. This paper proposes an MLP-based undersampling technique (MLPUS) which will preserve the distribution of information while doing undersampling. This technique uses stochastic measure evaluation for identifying important samples from the majority as well as minority samples. Experiments are performed on 5 real world data sets for the evaluation of performance of proposed work.


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Imbalanced Learning, Undersampling, Oversampling, Clustering.