document: Combining classifiers to identify online databases We address the problem of identifying the domain of onlinedatabases . More precisely , given a set F of Web forms automaticallygathered by a focused crawler and an online databasedomain D , our goal is to select from F only the formsthat are entry points to databases in D. Having a set ofWebforms that serve as entry points to similar online databasesis a requirement for many applications and techniques thataim to extract and integrate hidden-Web information , suchas meta-searchers , online database directories , hidden-Webcrawlers , and form-schema matching and merging . We propose a new strategy that automatically and accuratelyclassifies online databases based on features that canbe easily extracted from Web forms . By judiciously partitioningthe space of form features , this strategy allows theuse of simpler classifiers that can be constructed using learningtechniques that are better suited for the features of eachpartition . Experiments using real Web data in a representativeset of domains show that the use of different classifiersleads to high accuracy , precision and recall . This indicatesthat our modular classifier composition provides an effectiveand scalable solution for classifying online databases .
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