Improving Pseudobagging techniques

Authors:
Angela Chieppa;Karina Gibert;Ignasi Gómez-Sebastià;Miquel Sànchez-Marrè
Affiliations:
Statistics and Operations Research Department;Statistics and Operations Research Department;Computer Software Department, Technical University of Catalonia (UPC), Barcelona;Computer Software Department, Technical University of Catalonia (UPC), Barcelona
Venue:
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Year:
2008

Citing 4
Cited 0

Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

The Journal of Machine Learning Research
Combining Multiple Clusterings Using Evidence Accumulation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Short Communication: GESCONDA: An intelligent data analysis system for knowledge discovery and management in environmental databases

Environmental Modelling & Software
Estimation of the information by an adaptive partitioning of the observation space

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an important improvement related to the computation and use of Mutual Information index in Pseudobagging, a technique that adapts “bagging” to unsupervised context. The Mutual Information index plays a key role in this technique, assessing the quality of a partition. We propose the use of such an index to improve the Pseudobagging voting scheme for determining the final partition of the data. Issues related to the estimation of Mutual Information index in the multivariate continuous case become crucial for the application of Pseudobagging to real data: we discuss some practical approaches to computation in this situation. Finally, experimental results are presented, related to application of new “pooled voting” scheme and to the evaluation of the impact of different computing methods for Mutual Information.