Scalability analysis of ANN training algorithms with feature selection

Authors:
Verónica Bolón-Canedo;Diego Peteiro-Barral;Amparo Alonso-Betanzos;Bertha Guijarro-Berdiñas;Noelia Sánchez-Maroño
Affiliations:
Laboratory for Research and Development in Artificial Intelligence, Computer Science Dept., University of A Coruña, A Coruña, Spain;Laboratory for Research and Development in Artificial Intelligence, Computer Science Dept., University of A Coruña, A Coruña, Spain;Laboratory for Research and Development in Artificial Intelligence, Computer Science Dept., University of A Coruña, A Coruña, Spain;Laboratory for Research and Development in Artificial Intelligence, Computer Science Dept., University of A Coruña, A Coruña, Spain;Laboratory for Research and Development in Artificial Intelligence, Computer Science Dept., University of A Coruña, A Coruña, Spain
Venue:
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Year:
2011

Citing 10
Cited 1

Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems

Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Neurocomputing

Neurocomputing
Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning

Neural Networks
A Survey of Methods for Scaling Up Inductive Algorithms

Data Mining and Knowledge Discovery
Consistency-based search in feature selection

Artificial Intelligence
Efficient Feature Selection via Analysis of Relevance and Redundancy

The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)

Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Searching for interacting features

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset

Expert Systems with Applications: An International Journal

Toward the scalability of neural networks through feature selection

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The advent of high dimensionality problems has brought new challenges for machine learning researchers, who are now interested not only in the accuracy but also in the scalability of algorithms. In this context, machine learning can take advantage of feature selection methods to deal with large-scale databases. Feature selection is able to reduce the temporal and spatial complexity of learning, turning an impracticable algorithm into a practical one. In this work, the influence of feature selection on the scalability of four of the most well-known training algorithms for feedforward artificial neural networks (ANNs) is studied. Six different measures are considered to evaluate scalability, allowing to establish a final score to compare the algorithms. Results show that including a feature selection step, ANNs algorithms perform much better in terms of scalability.