Local Cascade Generalization
Joao Gama
LIACC, FEP - University of Porto
Rua Campo Alegre, 823
4150 Porto, Portugal
Phone: (+351) 2 678830 Fax: (+351) 2 6003654
Email: jgama@ncc.up.pt
http://www.up.pt/liacc/ML
Abstract
In a previous work we have presented Cascade Generalization,
a new general method for merging classifiers. The basic idea of
Cascade Generalization is to sequentially run the set of class ifiers,
at each step performing an extension of the original data by the
insertion of new attributes. The new attributes are derived from the
probability class distribution given by a base classifier. This
constructive step extends the representational language for the high
level classifiers, relaxing their bias. In this paper we extend the
previous work by applying Cascade locally. At each iteration of
a divide and conquer algorithm, a reconstruction of the instance
space occurs by the addition of new attributes.
Each new attribute is the probability that an example belongs to a
class given by a base classifier. We have implemented three {\em
Local Generalization Algorithms}. The first merges a linear
discriminant with a decision tree, the second merges a naive Bayes
with a decision tree, and the third merges a linear discriminant and a
naive Bayes with a decision tree. All the algorithms show an increase
of performance, when compared with the corresponding single models.
Cascade also outperforms other methods for combining classifiers, like
Stacked Generalization and competes well against Boosting, with
statistically significant confidence levels.
Keywords
Multiple Models, Constructive Induction, Merging Classifiers.