On feature relevance feedback methods : incorporating labeled user features Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/pc289m575

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  • In text classification, labeling features is often less time consuming than labeling entire documents. In situations where very little labeled training data is available, feature relevance feedback has the potential to dramatically increase classification performance. We review previous work on incorporating feature relevance feedback in the form of labeled features and introduce a new method, the Feature Contrast Method, for using feature relevance feedback with locally weighted logistic regression. We show that our method is responsive to user feedback and significantly outperforms previously developed feature relevance feedback methods while remaining robust to noise in feature and training data. We also highlight several key issues that affect the performance of feature feedback methods.
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