Statistical analysis with missing data
Statistical analysis with missing data
Model selection
Comparing connectionist and symbolic learning methods
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Quantifying the Resilience of Inductive Classification Algorithms
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
The lack of a priori distinctions between learning algorithms
Neural Computation
Auto-experimentation of KDD workflows based on ontological planning
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Improving effectiveness on clickstream data mining
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
Meta-learning for model selection, as reported in the symbolic machine learning community, can be described as follows. First, it is cast as a purely data-driven predictive task. Second, it typically relies on a mapping of dataset characteristics to some measure of generalization performance (e.g., error). Third, it tends to ignore the role of algorithm parameters by relying mostly on default settings. This paper describes a case-based system for model selection which combines knowledge and data in selecting a (set of) algorithm(s) to recommend for a given task. The knowledge consists mainly of the similarity measures used to retrieve records of past learning experiences as well as profiles of learning algorithms incorporated into the conceptual meta-model. In addition to the usual dataset characteristics and error rates, the case base includes objects describing the evaluation strategy and the learner parameters used. These have two major roles: they ensure valid and meaningful comparisons between independently reported findings, and they facilitate replication of past experiments. Finally, the case-based meta-learner can be used not only as a predictive tool but also as an exploratory tool for gaining further insight into previously tested algorithms and datasets.