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<p><b>Abstract</b>—We propose a new approach to text categorization known as generalized instance set (GIS) algorithm under the framework of generalized instance patterns. Our GIS algorithm unifies the strengths of k-NN and linear classifiers and adapts to characteristics of text categorization problems. It focuses on refining the original instances and constructs a set of generalized instances. We also propose a metamodel framework based on category feature characteristics. It has a metalearning phase which discovers a relationship between category feature characteristics and each component algorithm. Extensive experiments have been conducted on two large-scale document corpora for both GIS and the metamodel. The results demonstrate that both approaches generally achieve promising text categorization performance.</p>
Text classification, instance-based learning, metamodel learning.

Y. Han and W. Lam, "Automatic Textual Document Categorization Based on Generalized Instance Sets and a Metamodel," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 25, no. , pp. 628-633, 2003.
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