Adjusted Rand index is used to measure diversity in cluster ensembles
and a diversity measure is subsequently proposed.
Although the measure was found to be related to the quality of the ensemble,
this relationship appeared to be non-monotonic.
In some cases, ensembles which exhibited a moderate level of diversity
gave a more accurate clustering.
Based on this, a procedure for building a cluster ensemble of a chosen type is
proposed (assuming that an ensemble relies on one or more random parameters):
generate a small random population of cluster ensembles,
calculate the diversity of each ensemble
and select the ensemble corresponding to the median diversity.
We demonstrate the advantages of both our measure and procedure on
5 data sets and carry out statistical comparisons involving
two diversity measures for cluster ensembles from the recent literature.
An experiment with 9 data sets was also carried out to examine
how the diversity-based selection procedure fares
on ensembles of various sizes.
For these experiments the classification accuracy was used
as the performance criterion.
The results suggest that selection by median diversity is no worse
and in some cases is better than building and holding on to one ensemble.
ROC curves and video analysis optimization in intestinal capsule endoscopy
Summary:
Wireless capsule endoscopy involves inspection of hours of video material
by a highly qualified professional.
Time episodes corresponding to intestinal contractions, which are of interest
to the physician constitute about 1% of the video.
The problem is to label automatically time episodes containing contractions
so that only a fraction of the video needs inspection.
As the classes of contraction and non-contraction images in the video
are largely imbalanced, ROC curves are used to optimize the trade-off
between false positive and false negative rates.
Classifier ensemble methods and simple classifiers were examined.
Our results reinforce the claims from recent literature that
classifier ensemble methods specifically designed for imbalanced problems
have substantial advantages over simple classifiers
and standard classifier ensembles.
By using ROC curves with the bagging ensemble method the inspection time
can be drastically reduced at the expense of a small fraction
of missed contractions.
