A self-optimizing approach for knowledge acquisition with adaptively incremental sampling

The paper outlines a self-optimizing approach for knowledge acquisition with adaptively incremental sampling, which fused the self-optimizing approach for knowledge acquisition and sampling approaches in order to improve the efficiency of knowledge acquisition effectively. The proposed sampling approach enabled us to dynamically and adaptively adjust the sample size according to the data mining algorithm´s performance on the training samples so as to utilize the sample size as small as possible without reducing the accuracy of the knowledge model. Finally, the self-optimizing approach for knowledge acquisition with adaptively incremental sampling was applied to rule generation from the diagnostic decision table for rheumatoid arthritis in Chinese medical science. Experimentation results showed that the approach was much better than other algorithms both in efficiency and in accuracy