Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
Information-Based Evaluation Criterion for Classifier's Performance
Machine Learning
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Meta-Learner for Unknown Attribute Values Processing: Dealing with Inconsistency of Meta-Databases
Journal of Intelligent Information Systems
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Class noise vs. attribute noise: a quantitative study of their impacts
Artificial Intelligence Review
| Hi-index | 0.00 |
Real-world data usually contain a certain percentage of unknown (missing) attribute values. Therefore efficient robust data mining algorithms should comprise some routines for processing these unknown values. The paper [5] figures out that each dataset has more or less its own 'favourite' routine for processing unknown attribute values. It evidently depends on the magnitude of noise and source of unknownness in each dataset. One possibility how to solve the above problem of selecting the right routine for processing unknown attribute values for a given database is exhibited in this paper. The covering machine learning algorithm CN4 processes a given database for six routines for unknown attribute values independently. Afterwards, a meta-learner (meta-combiner) is used to derive a meta-classifier that makes up the overall (final) decision about the class of input unseen objects.The results of experiments with various percentages of unknown attribute values on real-world data are presented and performances of the meta-classifier and the six base classifiers are then compared.