Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data

<table class="table-group" width="800px"><tr class="fig-image" id="a"><td><img alt="875450.fig.007a" src="https://static.hindawi.com/articles/tswj/volume-2013/875450/figures/875450.fig.007a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>IRIS</td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="875450.fig.007b" src="https://static.hindawi.com/articles/tswj/volume-2013/875450/figures/875450.fig.007b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>IONOSPERE</td></tr></table></td></tr></table>

<table>Influence of stop parameter <svg height="7.9499998" id="M151" style="vertical-align:-0.1638pt" version="1.1" viewbox="0 0 6.5 7.9499998" width="6.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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</svg> to classification accuracy.</table>

The Scientific World Journal

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Figure 7

Figure 7: Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data 