COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Simple Random Sampling from Relational Databases
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Selective Sampling Based on the Variation in Label Assignments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Learning Deterministic Finite Automata with a Smart State Labeling Evolutionary Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introducing a Family of Linear Measures for Feature Selection in Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Active Coevolutionary Learning of Deterministic Finite Automata
The Journal of Machine Learning Research
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
Exploiting multiple classifier types with active learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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Selective sampling is a form of active learning which can reduce the cost of training by only drawing informative data points into the training set. This selected training set is expected to contain more information for modeling compared to random sampling, thus making modeling faster and more accurate. We introduce a novel approach to selective sampling, which is derived from the Estimation-Exploration Algorithm (EEA). The EEA is a coevolutionary algorithm that uses model disagreement to determine the significance of a training datum, and evolves a set of models only on the selected data. The algorithm in this paper trains a population of Artificial Neural Networks (ANN) on the training set, and uses their disagreement to seek new data for the training set. A medical data set called the National Trauma Data Bank (NTDB) is used to test the algorithm. Experiments show that the algorithm outperforms the equivalent algorithm using randomly-selected data and sampling evenly from each class. Finally, the selected training data reveals which features most affect outcome, allowing for both improved modeling and understanding of the processes that gave rise to the data.