Four Heads Are Better than One: Combining Suggestions for Case Adaptation

Leake, David; Kendall-Morwick, Joseph

doi:10.1007/978-3-642-02998-1_13

Cited by 6 publications

(2 citation statements)

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“…The first phase uses coarse-grained criteria to retrieve a set of initial candidates from the full database, restricting structural considerations to small independent sub-structures over which cases are indexed; the second phase considers the complete structure of each case retrieved from the first phase and re-ranks cases accordingly. To improve accuracy and robustness to noise, Phala uses multiple reasoning techniques to generate predictions and reconciles divergent predictions through a confidence-weighted voting scheme [9].…”

Section: Automatic Provenance Repairmentioning

confidence: 99%

A Noisy 10GB Provenance Database

Cheah

Plale

Kendall-Morwick

et al. 2012

Business Process Management Workshops

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Abstract. Provenance of scientific data is a key piece of the metadata record for the data's ongoing discovery and reuse. Provenance collection systems capture provenance on the fly, however, the protocol between application and provenance tool may not be reliable. Consequently, the provenance record can be partial, partitioned, and simply inaccurate. We use a workflow emulator that models faults to construct a large 10GB database of provenance that we know is noisy (that is, has errors). We discuss the process of generating the provenance database, and show early results on the kinds of provenance analysis enabled by the large provenance.

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Section: Automatic Provenance Repairmentioning

confidence: 99%

A Noisy 10GB Provenance Database

Cheah

Plale

Kendall-Morwick

et al. 2012

Business Process Management Workshops

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“…In fact, there is a belief in the area of computational intelligence that combining-modes system tends to minimize the disadvantages of single modes and maximize their advantages. 25 This idea has already been applied into the area of financial analysis, 26 housing value estimation, 10 cost prediction, 27 and process determination 28,29 with satisfying results. Enlightened by these, this study presents a new methodology to improve the performance of SFA for case-based mechanical design, namely, integrating multiple adaptation results provided by independent statistical adaptation methods to the combined solution.…”

Section: Introductionmentioning

confidence: 99%

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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