Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Operational rationality through compilation of anytime algorithms
Operational rationality through compilation of anytime algorithms
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
Expressiveness of $-Calculus: What Matters?
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
| Hi-index | 0.00 |
Feature selection is used to improve performance of learning algorithms by finding a minimal subset of relevant features. Since the process of feature selection is computationally intensive, a trade-off between the quality of the selected subset and the computation time is required. In this paper, we are presenting a novel, anytime algorithm for feature selection, which gradually improves the quality of results by increasing the computation time. The algorithm is interruptible, i.e., it can be stopped at any time and provide a partial subset of selected features. The quality of results is monitored by a new measure: fuzzy information gain. The algorithm performance is evaluated on several benchmark datasets.