One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

<table class="table-group" width="800px"><tr class="fig-image" id="a"><td><img alt="869628.fig.004a" src="https://static.hindawi.com/articles/mpe/volume-2014/869628/figures/869628.fig.004a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>Missing level (%)</td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="869628.fig.004b" src="https://static.hindawi.com/articles/mpe/volume-2014/869628/figures/869628.fig.004b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>Missing level (%)</td></tr></table></td></tr><tr class="fig-image" id="c"><td><img alt="869628.fig.004c" src="https://static.hindawi.com/articles/mpe/volume-2014/869628/figures/869628.fig.004c.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(c) </b>Missing level (%)</td></tr></table></td></tr><tr class="fig-image" id="d"><td><img alt="869628.fig.004d" src="https://static.hindawi.com/articles/mpe/volume-2014/869628/figures/869628.fig.004d.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(d) </b>Missing level (%)</td></tr></table></td></tr></table>

<table>The trend of classification performance in “German” dataset with MNAR type MVs.</table>

Mathematical Problems in Engineering

fig4

Figure 4

Figure 4: One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values 