Combination and optimization of classifiers in gender
classification using genetic programming
International Journal of Knowledge-Based and
Intelligent Engineering Systems, 9 (1):
1--11 (2005)Abstract
we have investigated the problem of gender
classification using frontal facial images. Four
different classifiers, namely K-means, k-nearest
neighbours, Linear Discriminant Analysis and
Mahalanobis Distance Based classifiers are compared.
Receiver operating characteristics (ROC) curve along
with the area under the convex hull (AUCH) have been
used as the performance measures of the classifiers at
different feature subsets. To measure the overall
performance of a classifier with single scalar value,
the new scheme of finding the area under the convex
hull of AUCH of ROC curves (AUCH of AUCHS) is proposed.
It has been observed that, when the number of macro
features is increased beyond 5, the AUCH saturates and
even decreases for some classifiers, illustrating the
curse of dimensionality. We then used genetic
programming to combine classifiers and thus evolved an
optimum combined classifier (OCC), producing better
performance than the individual classifiers. We found
that using only two features, the OCC has comparable
performance to that of original classifier using 20
macro features. It produces true positive rate values
as high as 0.94 corresponding to false positive rate as
low as 0.15 for 1: 3 train to testing ratio. We also
observed that heterogeneous combination of classifiers
is more promising than the homogenous combination.