A new adaptation method based on adaptability under k-nearest neighbors for case adaptation in case-based design

An adaptation phase is crucial for a good and reasonable case-based design (CBD) process, which is responsible for finding a solution to solve a new problem in the principle of k-nearest neighbors (k-NN). Statistical adaptation method is a classical method for feature-based case adaptation (FCA) because of its domain-independent and easily to be implemented, but with low adaptation accuracy. Therefore, this paper presents a new adaptation method for solution feature values of retrieved cases by introducing the adaptability value to improve the adaptation performance, called as adaptability-based FCA (AFCA). Unlike the classical statistical FCA method (SFCA) based on similarity or distance value, AFCA is performed in terms of the adaptability of old solution feature calculated by the adaptability measurement (AM) mechanism. A new AM method is studied as well in this paper, where the adaptability value for each solution feature is computed by utilizing the decision tree technique and similarity value, and the similarity is derived from the multi-algorithm-oriented hybrid SM strategy. Furthermore, to validate the feasibility and superiority of AFCA, the proposed method was applied to the power transformer design and was compared with the classical SFCAs. Empirical comparison results indicated that AFCA achieves the better adaptation performance under k-NN than other SFCAs on the basis of the adaptation accuracy.

► We propose a new adaptation method based on adaptability for old solution feature. ► An adaptability measurement way is studied by using decision tree and similarity. ► The solution features in most similar case may not be the most useful features. ► Adding similarity-weight will improve the performance of proposed method. ► The proposed adaptation method achieves better performance under 7-NN or 9-NN.