Regularized F-Measure Maximization for Feature Selection and Classification

<table>Performance with different <svg height="12.4375" id="M165" style="vertical-align:-0.1638pt" version="1.1" viewbox="0 0 9.5124998 12.4375" width="9.5124998" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,12.162)"><path d="M529 97q-70 -109 -136 -109q-41 0 -56 94q-23 144 -37 284q-38 -88 -99 -202.5t-93 -156.5q-26 -8 -76 -19l-9 21q71 78 145.5 193t124.5 232q-5 84 -15 128q-12 55 -29.5 75.5t-42.5 20.5q-21 0 -45 -13l-8 24q16 17 46 30t55 13q43 0 70 -46.5t40 -169.5
q27 -249 51 -392q7 -46 23 -46q24 0 70 60z" id="x1D706"></path></g>
</svg>s and number of
variables.</table>

BioMed Research International

fig6

Figure 6

Figure 6: Regularized F-Measure Maximization for Feature Selection and Classification 