C4.5: programs for machine learning
C4.5: programs for machine learning
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Ensemble Feature Selection Based on Contextual Merit and Correlation Heuristics
ADBIS '01 Proceedings of the 5th East European Conference on Advances in Databases and Information Systems
Ensemble Feature Selection Based on the Contextual Merit
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Decomposition of Heterogeneous Classification Problems
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Classification ensemble by genetic algorithms
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
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Recent research has proved the benefits of using an ensemble of diverse and accurate base classifiers for classification problems. In this paper the focus is on producing diverse ensembles with the aid of three feature selection heuristics based on two approaches: correlation and contextual merit -based ones. We have developed an algorithm and experimented with it to evaluate and compare the three feature selection heuristics on ten data sets from UCI Repository. On average, simple correlation-based ensemble has the superiority in accuracy. The contextual merit -based heuristics seem to include too many features in the initial ensembles and iterations were most successful with it.