<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Mining of imbalanced data isachallenging task due to its complex inherent characteristics. The conventional classifiers such as the nearest neighbor severely bias towards the majority class, as minority class data are under-represented and outnumbered. This paper focuses on building an improved Nearest Neighbor Classifier foratwo class imbalanced data. Three oversampling techniques are presented, for generation of artificial instances for the minority class for balancing the distribution among the classes. Experimental results showed that the proposed methods outperformed the conventional classifier.</p></abstract>

Mining of imbalanced data isachallenging task due to its complex inherent characteristics. The conventional classifiers such as the nearest neighbor severely bias towards the majority class, as minority class data are under-represented and outnumbered. This paper focuses on building an improved Nearest Neighbor Classifier foratwo class imbalanced data. Three oversampling techniques are presented, for generation of artificial instances for the minority class for balancing the distribution among the classes. Experimental results showed that the proposed methods outperformed the conventional classifier.

On Improving the Classification of Imbalanced Data

School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu,

School of Computing Science & Engineering, VIT University, Vellore, Tamil Nadu,

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"On Improving the Classification of Imbalanced Data"}

Mining of imbalanced data isachallenging task due to its complex inherent characteristics. The conventional classifiers such as the nearest neighbor severely...

On Improving the Classification of Imbalanced Data

Online veröffentlicht: 06. Apr. 2017

Seitenbereich: 45 - 62

DOI: https://doi.org/10.1515/cait-2017-0004

Schlüsselwörter
Imbalance data, nearest neighbor classifier, oversampling, synthetic data, Data Mining

© by Lincy Meera Mathews

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

On Improving the Classification of Imbalanced Data

Online veröffentlicht: 06. Apr. 2017

Seitenbereich: 45 - 62

DOI: https://doi.org/10.1515/cait-2017-0004

SchlüsselwörterImbalance data, nearest neighbor classifier, oversampling, synthetic data, Data Mining

© by Lincy Meera Mathews

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Imbalance data, nearest neighbor classifier, oversampling, synthetic data, Data Mining