Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
Data Mining and Knowledge Discovery
Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable, Distributed and Dynamic Mining of Association Rules
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Consistency-based search in feature selection
Artificial Intelligence
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Building predictors from vertically distributed data
CASCON '04 Proceedings of the 2004 conference of the Centre for Advanced Studies on Collaborative research
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)
Distributed prediction from vertically partitioned data
Journal of Parallel and Distributed Computing
Concept-based feature generation and selection for information retrieval
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
An incremental learning method for neural networks based on sensitivity analysis
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Expert Systems with Applications: An International Journal
Scalability analysis of ANN training algorithms with feature selection
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An Ensemble-Based Incremental Learning Approach to Data Fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Audio, Speech, and Language Processing
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In the past few years, the bottleneck for machine learning developers is not longer the limited data available but the algorithms inability to use all the data in the available time. For this reason, researches are now interested not only in the accuracy but also in the scalability of the machine learning algorithms. To deal with large-scale databases, feature selection can be helpful to reduce their dimensionality, turning an impracticable algorithm into a practical one. In this research, the influence of several feature selection methods on the scalability of four of the most well-known training algorithms for feedforward artificial neural networks (ANNs) will be analyzed over both classification and regression tasks. The results demonstrate that feature selection is an effective tool to improve scalability.