Algorithms for clustering data
Algorithms for clustering data
Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection for Meta-learning
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
A modal symbolic classifier for selecting time series models
Pattern Recognition Letters
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
Meta-learning techniques can be very useful for supporting non-expert users in the algorithm selection task. In this work, we investigate the use of different components in an unsupervised meta-learning framework. In such scheme, the system aims to predict, for a new learning task, the ranking of the candidate clustering algorithms according to the knowledge previously acquired. In the context of unsupervised meta-learning techniques, we analyzed two different sets of meta-features, nine different candidate clustering algorithms and two learning methods as meta-learners. Such analysis showed that the system, using MLP and SVR meta-learners, was able to successfully associate the proposed sets of dataset characteristics to the performance of the new candidate algorithms. In fact, a hypothesis test showed that the correlation between the predicted and ideal rankings were significantly higher than the default ranking method. In this sense, we also could validate the use of the proposed sets of meta-features for describing the artificial learning tasks.