Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
Characterizing the applicability of classification algorithms using meta-level learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Machine Learning
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Experiments in Meta-level Learning with ILP
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Quantifying the Resilience of Inductive Classification Algorithms
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Model selection via meta-learning: a comparative study
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
The lack of a priori distinctions between learning algorithms
Neural Computation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Layered concept-learning and dynamically variable bias management
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
An Analysis of Meta-learning Techniques for Ranking Clustering Algorithms Applied to Artificial Data
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A landmarker selection algorithm based on correlation and efficiency criteria
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
| Hi-index | 0.00 |
The selection of an appropriate inducer is crucial for performing effective classification. In previous work we presented a system called NOEMON which relied on a mapping between dataset characteristics and inducer performance to propose inducers for specific datasets. Instance-based learning was applied to meta-learning problems, each one associated with a specific pair of inducers. The generated models were used to provide a ranking of inducers on new datasets. Instance-based learning assumes that all the attributes have the same importance. We discovered that the best set of discriminating attributes is different for every pair of inducers.We applied a feature selection method on the meta-learning problems, to get the best set of attributes for each problem. The performance of the system is significantly improved.