Elements of information theory
Elements of information theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Dependency and Correlation Analysis for Features
IEEE Transactions on Knowledge and Data Engineering
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Expert Systems with Applications: An International Journal
Searching for interacting features
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Estimation of the information by an adaptive partitioning of the observation space
IEEE Transactions on Information Theory
General framework for class-specific feature selection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hardware-software platform for computing irreducible testors
Expert Systems with Applications: An International Journal
Feature extraction in protein sequences classification: a new stability measure
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Hi-index | 12.06 |
Feature selection has become an increasingly important field of research. It aims at finding optimal feature subsets that can achieve better generalization on unseen data. However, this can be a very challenging task, especially when dealing with large feature sets. Hence, a search strategy is needed to explore a relatively small portion of the search space in order to find ''semi-optimal'' subsets. Many search strategies have been proposed in the literature, however most of them do not take into consideration relationships between features. Due to the fact that features usually have different degrees of dependency among each other, we propose in this paper a new search strategy that utilizes dependency between feature pairs to guide the search in the feature space. When compared to other well-known search strategies, the proposed method prevailed.