Letters: Support vector machine interpretation

Authors:
A. Navia-Vázquez;E. Parrado-Hernández
Affiliations:
DTSC, Univ. Carlos III de Madrid, Avda Universidad 30, 28911-Leganés, Madrid, Spain;DTSC, Univ. Carlos III de Madrid, Avda Universidad 30, 28911-Leganés, Madrid, Spain
Venue:
Neurocomputing
Year:
2006

Citing 4
Cited 1

Making large-scale support vector machine learning practical

Advances in kernel methods
ANN-DT: an algorithm for extraction of decision trees from artificial neural networks

IEEE Transactions on Neural Networks
Weighted least squares training of support vector classifiers leading to compact and adaptive schemes

IEEE Transactions on Neural Networks
Extraction of rules from artificial neural networks for nonlinear regression

IEEE Transactions on Neural Networks

Comparing fine-grained source code changes and code churn for bug prediction

Proceedings of the 8th Working Conference on Mining Software Repositories

Quantified Score

Hi-index	0.01

Visualization

Abstract

Decisions taken by support vector machines (SVM) are hard to interpret from a human perspective. We take advantage of a compact SVM solution previously developed, known as growing support vector classifier (GSVC), to provide interpretation to SVM decisions in terms of input space segmentation in Voronoi sections (determined by the prototypes extracted during the GSVC training method) plus rules built as a linear combination of input variables. We show by means of experiments on public domain datasets that the resulting interpretable machines have high fidelity, and an accuracy comparable to the SVM.