Applying Ensemble Techniques for Improving the Performance of Rule-based Models in Data Mining
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Abstract
Modeling for high-performance forecasting is a challenging research. This research aims to enhance the performance of basic models including FURIA, MODLEM and RIPPER with popular integration techniques, including Bagging and Weighted Instances handler wrapper (WI). Data were collected from 699 breast cancer patients and 768 diabetic patients. In order to evaluate the prediction model, 10-fold cross validation was applied to divide dataset into training and testing sets. 10 experiments were conducted to reduce the bias of the experiment. sensitivity, specificity and accuracy were used to measure the predictive performance of the model generated by each technique. Based on the study, it was found that Bagging can increase the accuracy of breast cancer prediction by 4.91%.
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Articles published in Journal of Industrial Technology Ubon Ratchathani Rajabhat University both hard copy and electronically are belonged to the Journal.
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