QSVM : a support vector machine for rule extraction

Bologna, Guido (School of Engineering, Architecture and Landscape (hepia), HES-SO // University of Applied Sciences Western Switzerland) ; Hayashi, Yoichi (Departement of Computer Science, Meiji University, Tama-ku, Kawasaki, Japan)

Rule extraction from neural networks represents a difficult research problem, which is NP-hard. In this work we show how a special Multi Layer Perceptron architecture denoted as DIMLP can be used to extract rules from ensembles of DIMLPs and Quantized Support Vector Machines (QSVMs). The key idea for rule extraction is that the locations of discriminative hyperplanes are known, precisely. Based on ten repetitions of stratified 10-fold cross validation trials and with the use of default learning parameters we generated symbolic rules from five datasets. The obtained results compared favorably with respect to another state of the art technique applied to Support Vector Machines.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEPIA - Genève
Institute:
inIT - Institut d'Ingénierie Informatique et des Télécommunications
Publisher:
Palma de Mallorca, Spain, 10-12 June 2019
Date:
2015-06
Palma de Mallorca, Spain
10-12 June 2019
Cham
Springer
Pagination:
14 p.
Published in:
Lecture Notes in Computer Science ; Proceedings of International Work-Conference on Artificial Neural Networks 2015 (IWANN), 10-12 June 2015, Palama de Mallorca, Spain
Numeration (vol. no.):
pp. 276-289
DOI:
ISSN:
0302-9743
ISBN:
978-3-319-19221-5
Appears in Collection:



 Record created 2020-02-21, last modified 2020-02-28


Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)