The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized additive multi-mixture model for data mining
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Agent-Based Hybrid Intelligent Systems
Agent-Based Hybrid Intelligent Systems
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques
IEEE Transactions on Knowledge and Data Engineering
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Data mining of Bayesian networks using cooperative coevolution
Decision Support Systems
Combining classification algorithms
AI Communications
Expert Systems with Applications: An International Journal
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This paper presents a new hybrid classification method using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method tries to increase prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by two methods, and the extracted data subset is used to learn when each method works better. The learned discrimination model is called error pattern models and is used to merge the prediction results of two different methods to generate final prediction. The proposed method has been tested using 13 real-world data sets. The analysis results show that the performance of the proposed method is superior to other hybrid methods and the single usage of existing classification methods such as artificial neural networks and decision tree induction. In particular when prediction inconsistency ratio of the two methods is high, the proposed hybrid method provides significant improvement of prediction accuracy.