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Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling

Xiong, Hui
http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048

Abstract Details

2011, Doctor of Philosophy, Ohio State University, Industrial and Systems Engineering.
Engineers face many quality-related datasets containing free-style text or images. For example, a database could include summaries of complaints filed by customers, or descriptions of the causes of rework or maintenance or of the associated actions taken, or a collection of quality inspection images of welded tubes. The goal of this dissertation is to enable engineers to input a database of free-style text or image data and then obtain a set of clusters or “topics” with intuitive definitions and information about the degree of commonality that together helps prioritize system improvement. The proposed methods generate Pareto charts of ranked clusters or topics with their interpretability improved by input from the analyst or method user. The combination of subject matter expert data with standard data is the novel feature of the methods considered. Prior to the methods proposed here, analysts applied Bayesian mixture models and had limited recourse if the cluster or topic definitions failed to be interpretable or are at odds with the knowledge of subject matter experts. The associated “Subject Matter Expert Refined Topic” (SMERT) model permits on-going knowledge elicitation and high-level human expert data integration to address the issues regarding: (1) unsupervised topic models often produce results to user, and (2) to provide a “Hierachical Analysis Designed Latency Experiment” (HANDLE) for human expert to interact with the model results. If grouping are missing key elements, so-called “boosting” these elements is possible. If certain members of a cluster are nonsensical or nonphysical, so-called “zapping” these nonsensical elements is possible. We also describe a fast Collapsed Gibbs Sampling (CGS) algorithm for SMERT method, which offers the capacity to efficiently SMERT model large datasets but which is associated with approximations in certain cases. We use three case studies to illustrate the proposed methods. The first relates to scrap text reports for a Chinese manufacturer of stone products. The second relates to laser welding of tube joints and images characterizing bead shape. The third case study relates to consumer reports text user reviews of the Toyota Camry. The user reviews cover 10 years and the widely publicized acceleration issue. In all cases, the SMERT models help provide interpretable groupings of records in a way that could facilitate data-driven prioritization of improvement actions.
Theodore Allen, PhD (Advisor)
Suvrajeet Sen, PhD (Committee Member)
David Woods, PhD (Committee Member)
Computer Science; Engineering; Industrial Engineering; Information Technology
quality engineering; Bayesian mixture model; topic model; unstructured data; freestyle text; collapsed Gibbs sampling; text mining; data mining; human computer interaction; subject matter expert

Recommended Citations

Citations

  • Xiong, H. (2011). Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048

    APA Style (7th edition)

  • Xiong, Hui. Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling. 2011. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048.

    MLA Style (8th edition)

  • Xiong, Hui. "Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling." Doctoral dissertation, Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048

    Chicago Manual of Style (17th edition)

osu1312214048
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© 2011, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.