A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Elements of information theory
Elements of information theory
Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
On Feature Selection with Measurement Cost and Grouped Features
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cost-sensitive feature acquisition and classification
Pattern Recognition
Audio-based context recognition
IEEE Transactions on Audio, Speech, and Language Processing
Low-Complexity, Nonintrusive Speech Quality Assessment
IEEE Transactions on Audio, Speech, and Language Processing
On the Estimation of Differential Entropy From Data Located on Embedded Manifolds
IEEE Transactions on Information Theory
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Classification on mobile devices is often done in an uninterrupted fashion. This requires algorithms with gentle demands on the computational complexity. The performance of a classifier depends heavily on the set of features used as input variables. Existing feature selection strategies for classification aim at finding a "best" set of features that performs well in terms of classification accuracy, but are not designed to handle constraints on the computational complexity. We demonstrate that an extension of the performance measures used in state-of-the-art feature selection algorithms with a penalty on the feature extraction complexity leads to superior feature sets if the allowed computational complexity is limited. Our solution is independent of a particular classification algorithm.