Name
Abstract | ||
|---|---|---|
Due to the high number of insolvencies in the credit business, automatic procedures for testing the credit-worthiness of enterprises
become increasingly important. For this task we use classification trees with soft splits which assign the observations near
the split boundary to both branches. Tree models involve an extra complication as the number of parameters varies as the tree
grows and shrinks. Hence we adapt the reversible jump Markov Chain Monte Carlo procedure to this model which produces an ensemble
of trees representing the posterior distribution. For a real-world credit-scoring application our algorithm yields lower classification
errors than bootstrapped versions of regression trees (CART), neural networks, and adaptive splines (MARS). The predictive
distribution allows to assess the certainty of credit decisions for new cases and guides the collection of additional information.
|
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/BFb0094815 | PKDD |
Keywords | Field | DocType |
bayesian classification trees,model switching,soft splits,bayesian classification,regression tree,predictive distribution,classification tree,neural network,posterior distribution | Data mining,Bootstrapping,Computer science,Posterior probability,Artificial intelligence,Artificial neural network,Information processing,Regression,Naive Bayes classifier,Reversible-jump Markov chain Monte Carlo,Algorithm,Adaptive algorithm,Machine learning | Conference |
Volume | ISSN | ISBN |
1510 | 0302-9743 | 3-540-65068-7 |
Citations | PageRank | References |
1 | 0.43 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jörg Kindermann | 1 | 411 | 33.66 |
| Gerhard Paass | 2 | 1136 | 83.63 |