Building Reliable Metaclassifiers for Text Learning

Appropriately combining information sources to form a more effective output than any of the individual sources is a broad topic that has been researched in many forms. It can be considered to contain sensor fusion, distributed data-mining, regression combination, classifier combination, and even the basic classification problem. After all, the hypothesis a classifier emits is just a specification of how the information in the basic features should be combined. This dissertation addresses one subfield of this domain: leveraging locality when combining classifiers for text classification. Classifier combination is useful, in part, as an engineering aid that enables machine learning scientists to understand the difference in base classifiers in terms of their local reliability, dependence, and variance — much as higher-level languages are an abstraction that improves upon assembly language without extending its computational power. Additionally, using such abstraction, we introduce a combination model that uses inductive transfer to extend the amount of labeled data that can be brought to bear when building a text classifier combination model.