A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
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
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Identifying and eliminating mislabeled training instances
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Test feature classifiers: performance and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Learning by discovering conflicts
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
| Hi-index | 0.00 |
In many real-world situations there is no known method for computing the desired output from a set of inputs. A strategy for solving these type of problems is to learn the input-output functionality from examples. However, in such situations it is not known which information is relevant to the task at hand. In this paper we focus on selection of relevant examples. We propose a new noise elimination method which is based on the filtering of the so called pattern frequency domain and which resembles frequency domain filtering in signal and image processing. The proposed method is inspired by the bases selection algorithm. A basis is an irredundant set of relevant attributes. By identifying examples that are non-typical in bases determination, noise elimination is achieved. The empirical results show the effectiveness of the proposed example selection method on artificial and real databases.