The Strength of Weak Learnability
Machine Learning
Vector quantization and signal compression
Vector quantization and signal compression
Elements of information theory
Elements of information theory
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Mutual Information Theory for Adaptive Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Self-Organizing Maps
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Density-Based Data Reduction Algorithm for Robust Estimators
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Concept sampling: towards systematic selection in large-scale mixed concepts in machine learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A novel template reduction approach for the K-nearest neighbor method
IEEE Transactions on Neural Networks
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
Prototype reduction techniques: A comparison among different approaches
Expert Systems with Applications: An International Journal
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Data reduction algorithms determine a small data subset from a given large data set. In this article, new types of data reduction criteria, based on the concept of entropy, are first presented. These criteria can evaluate the data reduction performance in a sophisticated and comprehensive way. As a result, new data reduction procedures are developed. Using the newly introduced criteria, the proposed data reduction scheme is shown to be efficient and effective. In addition, an outlier-filtering strategy, which is computationally insignificant, is developed. In some instances, this strategy can substantially improve the performance of supervised data analysis. The proposed procedures are compared with related techniques in two types of application: density estimation and classification. Extensive comparative results are included to corroborate the contributions of the proposed algorithms.