Passenger-based predictive modeling of airline no-show rates
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Raising the baseline for high-precision text classifiers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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We develop a new concept for aggregating items of evidencefor class probability estimation. In Naïve Bayes, eachfeature contributes an independent multiplicative factor tothe estimated class probability. We modify this model to includean exponent in each factor in order to introduce fea-tureimportance. These exponents are chosen to maximizethe accuracy of estimated class probabilities on the trainingdata. For Naïve Bayes, this modification accomplishes morethan what feature selection can. More generally, since theindividual features can be the outputs of separate probabilitymodels, this yields a new ensemble modeling approach,which we call APM (Adjusted Probability Model), alongwith a regularized version called APMR.