Customized classification learning based on query projections

We develop a customized classification learning method QPL, which is based on query projections. Given an instance to be classified (query instance), QPL explores the projections of the query instance (QPs), which are essentially subsets of attribute values shared by the query and training instances. QPL investigates the associated training data distribution of a QP to decide whether it is useful. The final prediction for the query is made by combining some statistics of the selected useful QPs. Unlike existing instance-based learning, QPL does not need to compute a distance measure between instances. The utilization of QPs for learning can explore a richer hypothesis space and achieve a balance between precision and robustness. Another characteristic of QPL is that the target class may vary for different query instances in a given data set. We have evaluated our method with synthetic and benchmark data sets. The results demonstrate that QPL can achieve good performance and high reliability.

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