A Real-Valued Negative Selection Algorithm Based on Grid for Anomaly Detection

<table class="table-group" width="800px"><tr class="fig-image" id="a"><td><img alt="268639.fig.0015a" src="https://static.hindawi.com/articles/aaa/volume-2013/268639/figures/268639.fig.0015a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>Haberman’s Survival dataset </td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="268639.fig.0015b" src="https://static.hindawi.com/articles/aaa/volume-2013/268639/figures/268639.fig.0015b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>Abalone dataset </td></tr></table></td></tr><tr class="fig-image" id="c"><td><img alt="268639.fig.0015c" src="https://static.hindawi.com/articles/aaa/volume-2013/268639/figures/268639.fig.0015c.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(c) </b>Iris dataset  </td></tr></table></td></tr></table>

<table>ROC curves of RNSA, V-Detector, and GB-RNSA.</table>

Abstract and Applied Analysis

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Figure 15

Figure 15: A Real-Valued Negative Selection Algorithm Based on Grid for Anomaly Detection 