Ensemble Learning Techniques for Structured and Unstructured Data
| dc.contributor.author | King, Michael Allen | en |
| dc.contributor.committeechair | Abrahams, Alan Samuel | en |
| dc.contributor.committeechair | Ragsdale, Cliff T. | en |
| dc.contributor.committeemember | Wang, Gang Alan | en |
| dc.contributor.committeemember | Zobel, Christopher W. | en |
| dc.contributor.committeemember | Matheson, Lance A. | en |
| dc.contributor.department | Business Information Technology | en |
| dc.date.accessioned | 2015-04-02T08:00:07Z | en |
| dc.date.available | 2015-04-02T08:00:07Z | en |
| dc.date.issued | 2015-04-01 | en |
| dc.description.abstract | This research provides an integrated approach of applying innovative ensemble learning techniques that has the potential to increase the overall accuracy of classification models. Actual structured and unstructured data sets from industry are utilized during the research process, analysis and subsequent model evaluations. The first research section addresses the consumer demand forecasting and daily capacity management requirements of a nationally recognized alpine ski resort in the state of Utah, in the United States of America. A basic econometric model is developed and three classic predictive models evaluated the effectiveness. These predictive models were subsequently used as input for four ensemble modeling techniques. Ensemble learning techniques are shown to be effective. The second research section discusses the opportunities and challenges faced by a leading firm providing sponsored search marketing services. The goal for sponsored search marketing campaigns is to create advertising campaigns that better attract and motivate a target market to purchase. This research develops a method for classifying profitable campaigns and maximizing overall campaign portfolio profits. Four traditional classifiers are utilized, along with four ensemble learning techniques, to build classifier models to identify profitable pay-per-click campaigns. A MetaCost ensemble configuration, having the ability to integrate unequal classification cost, produced the highest campaign portfolio profit. The third research section addresses the management challenges of online consumer reviews encountered by service industries and addresses how these textual reviews can be used for service improvements. A service improvement framework is introduced that integrates traditional text mining techniques and second order feature derivation with ensemble learning techniques. The concept of GLOW and SMOKE words is introduced and is shown to be an objective text analytic source of service defects or service accolades. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:4594 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/51667 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | ensemble methods | en |
| dc.subject | data mining | en |
| dc.subject | Machine learning | en |
| dc.subject | classification | en |
| dc.subject | structured data | en |
| dc.subject | unstructured data | en |
| dc.title | Ensemble Learning Techniques for Structured and Unstructured Data | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Business, Business Information Technology | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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