2008 PartitionedLogisticRegressionfo

• (Chang et al., 2008) ⇒ Ming-wei Chang, Wen-tau Yih, and Christopher Meek. (2008). “Partitioned Logistic Regression for Spam Filtering.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401907

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• http://scholar.google.com/scholar?q=%22Partitioned+logistic+regression+for+spam+filtering%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401907&preflayout=flat#citedby

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Abstract

Naive Bayes and logistic regression perform well in different regimes. While the former is a very simple generative model which is efficient to train and performs well empirically in many applications,the latter is a discriminative model which often achieves better accuracy and can be shown to outperform naive Bayes asymptotically. In this paper, we propose a novel hybrid model, partitioned logistic regression, which has several advantages over both naive Bayes and logistic regression. This model separates the original feature space into several disjoint feature groups. Individual models on these groups of features are learned using logistic regression and their predictions are combined using the naive Bayes principle to produce a robust final estimation. We show that our model is better both theoretically and empirically. In addition, when applying it in a practical application, email spam filtering, it improves the normalized AUC score at 10% false-positive rate by 28.8% and 23.6% compared to naive Bayes and logistic regression, when using the exact same training examples.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 PartitionedLogisticRegressionfo	Wen-tau Yih Ming-wei Chang Christopher Meek			Partitioned Logistic Regression for Spam Filtering		KDD-2008 Proceedings		10.1145/1401890.1401907		2008