|
|
|
|
|
|
DF-SVM: a decision forest constructed on artificially
enlarged feature space by support vector machine
|
|
|
|
|
|
|
|
Zaman M. Faisal and Hideo Hirose
|
|
|
|
|
|
|
|
Artificial Intelligence Review, Vol.40, No.4, pp.467-494, 発行:
DEC 2013; 14 January 2012, pp.1-28. online
first 1/14/2012 doi:10.1007/s10462-011-9291-1
|
|
|
|
|
|
| Enlarging
the feature space of the base tree classifiers in a decision forest
by means of informative features extracted from an additional predictive
model is advantageous for classification tasks. In this paper,
we have empirically examined the performance of this type of decision
forest with three different base tree classifier models including;
(1) the full decision tree, (2) eight-node decision tree and (3)
two-node decision tree (or decision stump). The hybrid decision
forest with these base classifiers are trained in nine different
sized resampled training sets. We have examined the performance
of all these ensembles from different point of views; we have studied
the bias-variance decomposition of the misclassification error
of the ensembles, then we have investigated the amount of dependence
and degree of uncertainty among the base classifiers of these ensembles
using information theoretic measures. The experiment was designed
to find out: (1) optimal training set size for each base classifier
and (2) which base classifier is optimal for this kind of decision
forest. In the final comparison, we have checked whether the subsampled
version of the decision forest outperform the bootstrapped version.
All the experiments have been conducted with 20 benchmark datasets
from UCI machine learning repository. The overall results clearly
point out that with careful selection of the base classifier and
training sample size, the hybrid decision forest can be an efficient
tool for real world classification tasks. |
|
|
|
|
| decision
forest, tree node size, subsample ratio, bias-variance decomposition,
empirical analysis .
|
|
|
|
|
|
|
|
|
Times Cited in Web of Science:
Times Cited in Google Scholar:
Cited in Books:
WoS:
|
|
|
|
|
|
|
|