Ensemble learning based on randomized attribute selection and neighborhood covering reduction

Improving accuracy, robustness and understandability is the objective of classification modeling. Regarding instability and performance limitation of existing rule learning techniques, we introduce an ensemble classifier based on randomized neighborhood reduction and neighborhood covering reduction. A set of reducts are obtained with randomized attribute reduction. A collection of rule sets are derived from the reducts based on neighborhood covering reduction. And then the classification result is output by combining the classification decision of different rule sets. The experiment result shows that the proposed technique is better than or equal to other classifiers, and is more stable when deals with noisy data.