Proceedings

Authors Main tested method Sample composition Main results
Chen et al.
(2009)
MARS for feature selection
and SVM for classification.
Tsai (2009) t-statistic analysis for feature
selection and NNs for
classification.
Chaudhuri and
De (2010)
Fuzzy clustering for feature
selection and SVM for
classification.
Cho et al. (2010) DTs for feature selection and
CBR for classification.
Li et al. (2010) GA and statistical methods
for feature selection, CBR
for classification.
Ravisankar and
Ravi (2010)
t-statistic analysis and Group
Method of Data Handling
(GMDH) (A SOM model)
for feature selection. GMDH
for classification.
1130 good and 870 bad
credit applications.
Five datasets: 3 approx.
balanced, 1 with a 2/1 ratio
of good to bad cases and 1
resembles a real
population.
50 bankrupt firms matched
with 50 non-bankrupt.
500 healthy and 500
bankrupt firms.
135 healthy companies and
135 distressed ones.
Dataset 1: 29 healthy banks
and 37 bankrupt.
Dataset 2: 22 healthy banks
and 18 bankrupt.
Dataset 3: 64 healthy banks
and 65 bankrupt.
Dataset 4: 30 healthy banks
and 30 bankrupt.
~ 165 ~
The hybrid system
outperforms both several
individual approaches (CART,
SVM and MARS) and a
hybrid system which combines
SVM and CART.
The proposed model
outperforms benchmarking
systems.
The rating estimation done by
the model does not depend on
heuristics.
The proposed models
outperform Logit and NNs.
If a true optimal feature subset
is not used CBR could
possibly produce lower
performance.
The proposed models
outperform other neural
architectures.
Table 3. Studies on bankruptcy prediction using the EC approach
Authors Main tested method Sample composition Main results
Alfaro et al. Adaptive boosting (adaboost) 590 failed and 590 non- Adaboost outperforms NN.
(2008)
of CT.
failed firms.
Kim and Cho Ensemble of NN using 307 instances of
The model outperforms non-
(2008)
evolutionary computation creditworthy applications evolutionary ensembles of
techniques.
and 383 where it is not. NN.
Tsai and Wu Ensemble of several NN. Dataset 1:307 creditworthy Multiple NN classifiers do not
(2008)
applications and 383 not outperform a single best
creditworthy.
neural network classifier in
Dataset 2: 700 good and
300 bad credits.
Dataset 3: 307 good and
383 bad credits.
many cases.
Yu et al. (2008) Bootstrap aggregating Dataset 1: 357 good credit The proposed model
(bagging) of NN.
cases and 296 refused. consistently outperforms
Dataset 2: 30 failed and 30
non-failed firms.
single models and other EC.
Hung and Chen Stacking of algorithms based 56 bankrupt companies and The ensemble performs better
(2009)
on bankruptcy probabilities. 64 non-bankrupt
than other ensembles which
companies.
use the weighting or voting
strategy.
Karthik Chandra Boosting of multi layer 120 failed and 120 healthy Boosting yields results
et al. (2009). perceptron NN, CART, companies.
superior to those reported in
RandomForests, SVM and
previous studies on the same
logistic regression.
data set
Nanni and Random subspaces, class Same as in Tsai and Wu Random subspaces perform
Lumnini (2009) switching and rotation
forests.
(2008).
better than the other EC.
Yu et al. (2010) Ensemble of SVM devices. 902 good loans and 323 The proposed ensemble
bad cases.
consistently outperforms other
ensemble models and five
single systems.