Abstract:
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This talk explores ordinal classification for unstructured predictors with ordered class categories, where imprecise information concerning strengths between predictors is available for predicting class labels. However, imprecise information here is expressed in terms of a directed graph, with each node representing a predictor and a directed edge containing pairwise strengths between two nodes. One of the targeted applications for unstructured data arises from sentiment analysis, which identifies and extracts the relevant content or opinion of a document concerning a specific event of interest. We integrate the imprecise predictor relations into linear relational constraints over classification function coefficients, where large margin ordinal classifiers are introduced, subject to many quadratically linear constraints. We implement ordinal support vector machines and $\psi$-learning through a scalable quadratic programming package based on sparse word representations. Theoretically, we also show that utilizing relationships among unstructured predictors improves prediction accuracy of classification significantly.
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