Exploring Query Matrix for Support Pattern Based Classification Learning
This paper explores the customized learning from specific to general for classification learning. Our novel learning framework called SUPE customizes its learning process to the instance to be classified called query instance. The data representation in SUPE is also customized to the query instance. Given a query instance, the training data is transformed into a query matrix, from which useful patterns are discovered for learning. The final prediction of the class label is performed by combining some statistics of the discovered useful patterns. We show that SUPE conducts the search from specific to general in a significantly reduced hypothesis space. The query matrix also facilitates the complicated operations in SUPE. The experimental results on benchmark data sets are encouraging.
KeywordsClass Label Training Instance Hypothesis Space Support Pattern Query Instance
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