document: Local sparsity control for naive Bayes with extreme misclassification costs In applications of data mining characterized by highly skewed misclassification costs certain types of errors become virtually unacceptable . This limits the utility of a classifier to a range in which such constraints can be met . Naive Bayes , which has proven to be very useful in text mining applications due to high scalability , can be particularly affected . Although its 0\/1 loss tends to be small , its misclassifications are often made with apparently high confidence . Aside from efforts to better calibrate Naive Bayes scores , it has been shown that its accuracy depends on document sparsity and feature selection can lead to marked improvement in classification performance . Traditionally , sparsity is controlled globally , and the result for any particular document may vary . In this work we examine the merits of local sparsity control for Naive Bayes in the context of highly asymmetric misclassification costs . In experiments with three benchmark document collections we demonstrate clear advantages of document-level feature selection . In the extreme cost setting , multinomial Naive Bayes with local sparsity control is able to outperform even some of the recently proposed effective improvements to the Naive Bayes classifier . There are also indications that local feature selection may be preferable in different cost settings .
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