Browsing by Author "TAN, Yee Fan"

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    Cost-sensitive Attribute Value Acquisition for Support Vector
    (2010-03-30T06:40:59Z) TAN, Yee Fan; KAN, Min-Yen
    We consider cost-sensitive attribute value acquisition in classification problems, where missing attribute values in test instances can be acquired at some cost. We examine this problem in the context of the support vector machine, employing a generic, iterative framework that aims to minimize both acquisition and misclassification costs. Under this framework, we propose an attribute value acquisition algorithm that is driven by the expected cost savings of acquisitions, and for this we propose a method for estimating the misclassification costs of a test instance before and after acquiring one or more missing attribute values. In contrast to previous solutions, we show that our proposed solutions generalize to support vector machines that use arbitrary kernels. We conclude with a set of experiments that show the effectiveness of our proposed algorithm.
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    Extending corpus-based identification of light verb constructions using a supervised learning framework
    (2005-08-19T02:32:51Z) TAN, Yee Fan; KAN, Min-Yen; CUI, Hang
    Light verb constructions (LVC) such as "make a call" and "give a presentation" pose challenges for natural language processing and understanding. We propose corpus-based methods to automatically identify LVCs. We extend existing corpus-based measures for identifying LVCs among verb-object pairs, using new features that use mutual information and assess the influence of other words in the context of a candidate verb-object pair, such as nouns and prepositions. To our knowledge, our work is the first to incorporate both existing and new LVC features into a unified machine learning approach. We experimentally demonstrate the superior performance of our framework and the effectiveness of the newlyproposed features.
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    A Framework for Hierarchical Cost-sensitive Web Resource Aquisition
    (2010-03-30T06:33:05Z) TAN, Yee Fan; KAN, Min-Yen
    Many record matching problems involve information that is insufficient or incomplete, and thus solutions that classify which pairs of records are matches often involve acquiring additional information at some cost. For example, web resources impose extra query or download time. As the amount of resources that can be acquired is large, solutions invariably acquire only a subset of the resources to achieve a balance between acquisition cost and benefit. At the same time, resources often have hierarchical dependencies between themselves, e.g., the search engine results for two queries must be obtained before the TF-IDF cosine similarity between their snippets can be computed. We propose a framework for performing cost-sensitive acquisition of resources with hierarchical dependencies, and apply it to the web resource context. Our framework is versatile, applicable to a large variety of problems. We show that many problems involving selective resource acquisitions can be formulated using resource dependency graphs. We then solve the resource acquisition problem by casting it as a combinatorial search problem. As the support vector machine is commonly used to effectively solve record matching problems, we also propose a benefit function that works with this classifier. Finally, we demonstrate the effectiveness of our acquisition framework on record matching problems.