Multivariable Case Adaptation Method of Case-Based Reasoning Based on Multi-Case Clusters and Multi-Output Support Vector Machine for Equipment Maintenance Cost Prediction

Mingchi Lin,Dubo He,Shengxiang Sun

doi:10.1109/access.2021.3117585

Abstract

Case adaptation is crucial for case-based reasoning (CBR) because the solutions of old cases are not always the ideal answer for the encountered new problem. It is employed to solve new problems by utilizing the adaptation knowledge extracted from similar ones encountered in the past. The traditional adaptation method solves a new problem in the principle of k- nearest neighbor ( $k$ -NN), and the adaptation model was built based on $k$ similar cases. Yet, the $k$ similar cases retrieved by new case may locate in different case clusters in the case base composed of multiple case clusters. This article presents a new case adaptation method by the combination of multi-adaptation engines from different case clusters to improve the adaptation accuracy. First, the input and output of the cluster-based adaptation engine are established from the old cases to distill the adaptation knowledge in each case cluster. Then, the multivariable CBR adaptation engine based on multiple-output support vector regression (MSVR) is built for case adaptation. Furthermore, inspired by the fact that the training sample which contains two closet cases can provide more useful information than others, and reduce the impact of outliers, this study adds the hybrid weight into MSVR, and allocates high weights to the information provided by such high sample density and similarity samples during multi-dimensional regression estimation. Finally, the solution of the target case is gathered by incorporating the output of different adaptation engines. The proposed method was applied to the equipment maintenance cost prediction and compared with traditional statistical-based and machine learning-based methods. Empirical comparison results indicated that the proposed adaptation method could achieve the best performance by utilizing the adaptation knowledge in different clusters under multi-case clusters environment.

Highlights

Case-based reasoning (CBR) is a problem-solving paradigm that remembers previous similar situations and reuses information and knowledge about the stored cases for dealing with new problems[1]
Existing CBR systems are generally characterized by a sophisticated case retrieval mechanism without a well-developed case adaptation engine, this resulted from the fact that case adaptation needs to be guided by domain knowledge or specialists experience, while adaptation knowledge is not always accessible and available[3]
For different target cases, case adaptation accuracies obtained through the combination of multiple-clustersbased case adaptation models is higher than the adaptation result based on single case cluster, the adaptation of related case clusters (RCCs)-III-III Synthesize the adaptation information of similar cases in different case clusters, making its adaptation accuracy significantly higher than that of single case cluster Related Case Cluster I (RCC-I), Related Case Cluster II (RCC-II) and Related Case Cluster III (RCC-III), because the selected case is at the

Summary

INTRODUCTION

Case-based reasoning (CBR) is a problem-solving paradigm that remembers previous similar situations (or cases) and reuses information and knowledge about the stored cases for dealing with new problems[1]. When applying the ML-based adaptation method, the adaptation knowledge acquirement is performed in similar case set obtained from case retrieval process. Case retrieval generally falls into one of three categories: k-nearest neighbor (k-NN), inductive learning, and knowledge guide Among these methods, the k-NN search method is most frequently used for selecting similar past cases in the CBR system, and the k-NN method retrieves k cases based on the weighted sum of case features in the problem case against the cases in memory[17]. Under the multi-RCCs scenario, the number of DSCs in different case clusters is diverse, the case adaptation knowledge based on different RCCs should not be implemented in the new problem equivalently. This paper intends to introduce a modularization technique into CBR adaptation, and the adaptation engine is performed into multi-case clusters according to the results of case retrieval. The adaptation model assembled by each adaptation engine is used to derive the target case solution

SPECIFICATION ON PROPOSED METHOD

ADAPTATION ENGINE CONSTRUCTION BY HYBRID WEIGHTED MULTI-OUTPUT SVM

EXPERIMENTS

68 MSVR-SW

CONCLUSION