Iterative reordering of rules for building ensembles without relearning

• Type: Chapter or Part of a Book
• Date: 2007

Authors

Paulo J Azevedo (Author) Other; Alipio M Jorge (Author) FEP

Book

Title: Discovery Science

Pages: 56-67

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-004-CYD

DOI: 10.1007/978-3-540-75488-6_7

Abstract (EN): We study a new method for improving the classification accuracy of a model composed of classification association rules (CAR). The method consists in reordering the original set of rules according to the error rates obtained on a set of training examples. This is done iteratively, starting from the original set of rules. After obtaining N models these are used as an ensemble for classifying new cases. The net effect of this approach is that the original rule model is clearly improved. This improvement is due to the ensembling of the obtained models, which are, individually, slightly better than the original one. This ensembling approach has the advantage of running a single learning process, since the models in the ensemble are obtained by self replicating the original one.

Language: English

Type (Professor's evaluation): Scientific

Notes: Part of the Lecture Notes in Computer Science book series (LNCS, volume 4755) Print ISBN: 978-3-540-75487-9

No. of pages: 12