Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation

Qi, Jin; Hu, Jie; Peng, Yinghong

doi:10.1016/j.engappai.2014.09.010

Cited by 15 publications

(19 citation statements)

References 40 publications

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“…Inspired by the empirical findings, [49][50][51] radial basis kernel exp( À gjjx i À x j jj 2 ) is used as the kernel function of MSFA-SVR because of its good performance under the general smoothness assumptions. Following previous SVR studies, 38,42,43,52 the performance of SVR is insensitive to e, and the reasonable values of e are 0.01. Meanwhile, two parameters should be tuned to construct SVR model, that is, C and g. For C and g, it is not known beforehand which values of C and g are the best for one problem, where C determines the trade-offs between minimizing fitting errors and minimizing model complexity.…”

Section: Svr Model Constructionmentioning

confidence: 69%

Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

2018

Advances in Mechanical Engineering

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Case adaptation is crucial for a good and reasonable case-based design which is a common method in computer-aided design, because the solution of old case is not always the exact answer for the encountered new designing problem. Recently, a statistical feature-oriented adaptation method in the principle of k-nearest neighbors has been employed widely in case-based design because of its easily understandable mechanism for designers, but its adaptation accuracy is relatively low compared with knowledge-intensive method. This article presents a new method by integrating with multiple adaptation values from statistical feature-oriented adaptation methods to improve the adaptation accuracy. First, two simple statistical feature-oriented adaptation methods including mean and median approaches and other four statistical feature-oriented adaptation methods based on Euclidean distance, Manhattan distance, Gaussian transformation, and gray coefficient are used as individual statistical feature-oriented adaptation mode in this article, and each statistical feature-oriented adaptation method generates pre-adapting value under k-nearest neighbor. Then, support vector regression is utilized to make a combined adaptation which takes pre-adapting values as its inputs, and the new multiple statistical feature-oriented adaptation method integration based on support vector regression is named as MSFA-SVR. Furthermore, to validate the feasibility and superiority of MSFA-SVR, it was applied to the power transformer design and was compared with the classical statistical feature-oriented adaptation methods. Empirical comparison results indicated that MSFA-SVR achieves the better adaptation performance under k-nearest neighbor than other statistical feature-oriented adaptation methods in terms of adaptation accuracy.

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Section: Svr Model Constructionmentioning

confidence: 69%

Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

2018

Advances in Mechanical Engineering

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“…So, we have proposed an MSVR-based adaptation approach in our previous study. 47 We adopt the differential heuristic [36][37][38]48 to build the training sample of MSVR. By using differential heuristic, the training sample consists of the differences between extracted case and retrieved case, rather than a single case in case base.…”

Section: Motivation and Originality Of This Researchmentioning

confidence: 99%

“…However, like the conventional SVRbased adaptation, the empirical error of each training sample in MSVR is equally penalized, which means every sample affects the generalization ability equally. 37 However, actually, different training sample has different effect for MSVR modelization. That is, training sample which contains two closer cases is more useful for MSVR.…”

Section: Motivation and Originality Of This Researchmentioning

confidence: 99%

“…34,35 Hence, some studies have employed SVR to automatically perform CBR adaptation with various degrees of success. [36][37][38] Despite the SVR has been employed preliminarily in CBR adaptation, the applications of SVR in CBR adaptation also expose a potential problem, namely, the standard formulation of SVR can only be used as a univariate modelling technique for CBR adaptation due to its inherent single-output structure. 39 Consequently, on one hand, SVR-based adaptation studies highlight the superiority of SVR for CBR adaptation.…”

Section: Introductionmentioning

confidence: 99%

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Multivariable case-based reason adaptation based on multiple-output support vector regression with similarity-related weight for parametric mechanical design

2018

Advances in Mechanical Engineering

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Using historical cases' solutions to obtain feasible solution for new problem is fundamentally to successfully applying case-based reason technique in parametric mechanical design. As a well-known intelligent algorithm, the formulation of support vector regression has been taken for case-based reason adaptation, but the standard support vector regression can only be used as a univariate adaptation method because of its single-output structure, which would result in the ignorance of the possible interrelations among solution outputs. To handle the complicated case adaptation task with large number of problem inputs and solution outputs more efficiently, this study investigates the possibility of multivariable case-based reason adaptation with multiple output by applying multiple-output support vector regression. Furthermore, inspired by the fact that training sample which contains two closer cases can provide more useful information than others, this study adds the similarity-related weight into multiple-output support vector regression and gives high weights to the information provided by such useful training sample during multi-dimensional regression estimation. The superiority of proposed multiple-output support vector regression with similarity-related weight is validated by the actual design example and quantitative comparisons with other adaptation methods. The comparative results indicate that multiple-output support vector regression with similarity-related weight achieves the best performance for largequantity case-based reason adaptation because of its higher accuracy and relatively lower cost.

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“…Further, referring to color trends, these CIWs and the CRs of the proposed system will be updated. The proposed system is an open system, which is capable of dynamically dealing with user's color image perceptual data and integrating the latest color trends [26] . Color design rules are extracted by designers using their professional knowledge through a series of human evaluation experiments [7] .…”

Section: Introductionmentioning

confidence: 99%

CBCRS: An open case-based color recommendation system

Hong

Zeng

Wang

et al. 2018

Knowledge-Based Systems

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In this paper, a case-based color recommendation system (CBCRS) is proposed for online color ranges (CRs) recommendation. This system can help designers and consumers to obtain the most appropriate CR of consumer-products (e.g., garments, cars, architecture, furniture …) based on the color image perceptual data of each specific user. The proposed system is an open system, permitting to dynamically integrate new CRs by progressively learning from users' and designers' perceptual data. For this purpose, a Color Image Space (CIS) is initially established by using Basic Color Sensory Attributes (BCSAs) to obtain the color image perceptual data of both designers and consumers. Emotional Color Image Words (CIWs) representing CRs are measured in the proposed CIS through a knowledge-based Kansei evaluation process performed by designers using fuzzy aggregation operators and fuzzy similarity measurement tools. Using this method, new CIWs and related CRs from open resources (such as new color trends) can be integrated into the system. In a new recommendation, user's color image perceptual data measured in the proposed CIS regarding different BCSAs will be compared with those of CIWs previously defined in the system in order to recommend new CRs. CBCRS is an adaptive system, i.e. satisfied CRs will be further retained in a Successful Cases Database (SCD) so as to adapt recommended CRs to new consumers, who have similar user profiles. The general working process of the proposed system is based on case-based learning. Through repeated interactions with the proposed system by performing the cycle of Recommendation-Display-Evaluation-SCD adjustment , users (consumer or designer) will obtain satisfied CRs. Meanwhile, the quality of the SCD can be improved by integrating new recommendation cases. The proposed recommendation system is capable of dynamically generating new CIWs, CRs and new cases based on open resources.

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Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation

Cited by 15 publications

References 40 publications

Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

Multivariable case-based reason adaptation based on multiple-output support vector regression with similarity-related weight for parametric mechanical design

CBCRS: An open case-based color recommendation system

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