Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling
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The Ohio State University / OhioLINK
2011
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-osu13122140482021-08-03T06:03:32Z Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling Xiong, Hui 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 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. 2011-09-26 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048 http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
| collection |
NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
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 |
| spellingShingle |
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 Xiong, Hui Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling |
| author |
Xiong, Hui |
| author_facet |
Xiong, Hui |
| author_sort |
Xiong, Hui |
| title |
Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling |
| title_short |
Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling |
| title_full |
Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling |
| title_fullStr |
Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling |
| title_full_unstemmed |
Combining Subject Expert Experimental Data with Standard Data in Bayesian Mixture Modeling |
| title_sort |
combining subject expert experimental data with standard data in bayesian mixture modeling |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2011 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1312214048 |
| work_keys_str_mv |
AT xionghui combiningsubjectexpertexperimentaldatawithstandarddatainbayesianmixturemodeling |
| _version_ |
1719430137465798656 |