Mixed data object selection based on clustering and border objects

Authors:
J. Arturo Olvera-López;J. Francisco Martínez-Trinidad;J. Ariel Carrasco-Ochoa
Affiliations:
Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

Citing 7
Cited 1

C4.5: programs for machine learning

C4.5: programs for machine learning
Reduction Techniques for Instance-BasedLearning Algorithms

Machine Learning
Clustering by Scale-Space Filtering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Instance Selection for Instance-Based Learning Algorithms

Data Mining and Knowledge Discovery
Extension to C-means Algorithm for the Use of Similarity Functions

PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Rapid and brief communication: A clustering method for automatic biometric template selection

Pattern Recognition
Transforming strings to vector spaces using prototype selection

SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition

Automatic discovery of Web Query Interfaces using machine learning techniques

Journal of Intelligent Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In supervised classification, the object selection or instance selection is an important task, mainly for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new mixed data object selection method based on clustering and border objects. We carried out an experimental comparison between our method and other object selection methods using some mixed data classifiers.