Aijun Yan

TL;DR: Experimental results over some benchmark datasets and a fault diagnosis of the Tennessee-Eastman (TE) process demonstrate that the proposed reasoning techniques can effectively improve the classification accuracy, and the LPM-based retrieval method can substantially improve the quality and learning ability of CBR classifiers.

TL;DR: Experiments and applications show that the ADR method is effective and the fitting error of theADR-based CBR (ADRCBR) model is significantly lower than other typical regression methods, indicating that ADR can improve the learning performance of the CBR model and has the advantage of application.

TL;DR: A dynamic maintenance method improved by selective memory and intentional forgetting for CBR is proposed, which can imitate the memory function of the human brain to selectively save new cases, update the forgotten values and intentionally delete the old cases, thus improving the performance of CBR.

TL;DR: An improved case evaluation method is employed to evaluate the trustworthiness of the suggested solution after the reuse step, which will divide the target cases and its suggested solutions into a trustworthy set and an untrustworthy set in accordance with a threshold value of trustworthiness.