Prediction markets have been demonstrated to be accurate predictors of the outcomes of future events. They have been successfully used to predict the outcomes of sporting events, political elections and even business decisions. Their prediction accuracy has even outperformed the accuracy of other prediction methods such as polling. As an attempt to reproduce their predictive capability, a machine learning model of prediction markets is developed herein for classification. This model is a novel classifier aggregation technique that generalizes linear aggregation techniques. This prediction market aggregation technique is shown to outperform or match Random Forest on both artificial and real data sets. The notion of specialization is also developed and explored herein. This leads to a new kind of classifier referred to as a specialized classifier. These specialized classifiers are shown to improve the accuracy of prediction market aggregation even to perfection. / A Thesis submitted to the Department of Scientiļ¬C Computing in partial fulfillment of the requirements for the degree of Master of Science. / Fall Semester, 2009. / November 5, 2009. / Machine Learning, Aggregation, Random Forest / Includes bibliographical references. / Adrian Barbu, Professor Directing Thesis; Anke Meyer-Baese, Committee Member; Tomasz Plewa, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_181557 |
| Contributors | Lay, Nathan (authoraut), Barbu, Adrian (professor directing thesis), Meyer-Baese, Anke (committee member), Plewa, Tomasz (committee member), Department of Scientific Computing (degree granting department), Florida State University (degree granting institution) |
| Publisher | Florida State University, Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text |
| Format | 1 online resource, computer, application/pdf |
| Rights | This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. |
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