Learning multiple descriptions for each class in the data has been shown to reduce
generalization error but the amount of error reduction varies greatly from domain to domain …

Many successful inductive learning systems use a propositional attribute-value language to
represent both training examples and induced hypotheses. Recent developments are …

The idea of decomposition methodology is to break down a complex Data Mining task into
several smaller, less complex and more manageable, sub-tasks that are solvable by using …

The idea of ensemble methodology is to build a predictive model by integrating multiple
models. It is well-known that ensemble methods can be used for improving prediction …

Cana& Page 1 1+1 National Library of Canada Bibliotheque nationals du Canada Acquisitions
and Bibliographic Services Branch 395 Wellington Street Ottawa, Ontario K1A ON4 NOTICE …

Esta tese explora diferentes aspectos da metodologia de indução de árvores de regressão
a partir de amostras de dados. O objectivo principal deste estudo é o de melhorar a …

Stacking is a general ensemble method in which a number of base classifiers are combined
using one meta-classifier which learns their outputs. Such an approach provides certain …

Occam's razor has been the subject of much controversy. This paper argues that this is partly
because it has been interpreted in two quite different ways, the first of which (simplicity is a …

A bstract Ensemble learning is a technique where multiple component learners are
combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the …

The principle and experimental results of an attribute oriented rough set approach for
knowledge discovery in databases are described. Our method integrates the database …