On Improving the Classification of Imbalanced Data

Mathews, Lincy; Setiapraja, Hari

doi:10.1515/cait-2017-0004

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…We have selectively taken a very few contributions which are under the domain of class imbalance learning. Lincy Meera Mathews et al, [16] have proposed an improved Nearest Neighbor Classifier for a two class imbalanced data using three oversampling techniques for generation of artificial instances for the minority class for balancing the distribution among the classes. Sadam Al-Azani et al, [17] compared the performance of different classifiers for polarity determination in highly imbalanced short text datasets using features learned by word embedding rather than hand-crafted features.…”

Section: Related Workmentioning

confidence: 99%

A novel k-nearest neighbor distance based under sampling for improved opinion mining on skewed data using random forest

Babu¹,

Battula²

2018

IJET

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In recent years, consumers are performing a pilot investigation using online resources before making any decision of purchase. One of the most popular social blogging online medium is twitter. The opinions collected from twitter at any point of frame in real world scenario are tending towards class imbalance in nature. The existing algorithms for opinion mining can work better on class balance nature, where opinions (positive and negative) are almost balance. In this paper, we propose a novel approach known as Improved Opinion Mining using Under Sampling (IOMUS) to efficiently summarize the reviews of class imbalance opinion mining corpus. The experimental set up is performed on the collection of opinion mining class imbalance dataset consisting of "1155" instances. The experimental results suggest that improved performance is obtained by the proposed IOMUS algorithm than the traditional approach.

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Section: Related Workmentioning

confidence: 99%