Technical Report CIS-2005-04

Title: Self-consistent Batch-Classification
Authors: Shaul Markovitch and Oren Shnitzer
Abstract: Most existing learning algorithms generate classifiers that take as an input a single untagged instance and return its classification. When given a set of instances to classify, the classifier treats each member of the set independently. In this work we introduce a new setup we call \emph{batch classification}. In this setup the induced classifier receives the \emph{testing} instances as a set. Knowing the test set in advance theoretically allows the classifier to classify it more precisely. We study the batch classification framework and develop learning algorithms that take advantage of this setup. We present several KNN-based solutions \citep{FixHodges51, dudahart} that combine the nearest-neighbor rule with some additions that allow it to use the additional information about the test set. Extensive empirical evaluation shows that these algorithms indeed outperform traditional independent classifiers.
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