Elements of information theory
Elements of information theory
Fundamentals of algorithmics
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Feature Selection via Set Cover
KDEX '97 Proceedings of the 1997 IEEE Knowledge and Data Engineering Exchange Workshop
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Dynamic Programming
An introduction to variable and feature selection
The Journal of Machine Learning Research
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
An empirical comparison of three boosting algorithms on real data sets with artificial class noise
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Sequential feature selection for classification
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Dynamic security contingency screening and ranking using neural networks
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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The classification of power system operating states plays an important role in power system control and operation. Determining the state of a power system is crucial and requirements for real-time decision making in power system security assessment demand low dimensionality and low computational time. This paper investigates the benefits of using feature selection based on mutual information in power system state classification with machine learning. The AdaBoost algorithm is used for classification based on large training datasets and feature selection is applied in order to reduce their dimensionality. The selection is implemented as a filter in the pre-processing stage of AdaBoost and uses genetic algorithms to perform the search with the fitness function computed based on mutual information. The proposed method is tested on the IEEE New England 39-bus network and a comparison between the learning algorithm performances with and without feature selection is provided. Results for different genetic algorithm parameters are also presented.