Making Logistic Regression A Core Data Mining Tool With TR-IRLS (2005)
Tags
Applications, Efficient Statistical Algorithms, Optimization
Abstract
Binary classification is a core data mining task. For large datasets or real-time applications, desirable classifiers are accurate, fast, and need no parameter tuning. We present a simple implementation of logistic regression that meets these requirements. A combination of regularization, truncated Newton methods, and iteratively re-weighted least squares make it faster and more accurate than modern SVM implementations, and relatively insensitive to parameters. It is robust to linear dependencies and some scaling problems, making most data preprocessing unnecessary.
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Approximate BibTeX Entry
@inproceedings{komarek:icdm2005,
Howpublished = {InProceedings},
Year = {2005},
Pages = {4},
Booktitle = {Proceedings of the 5th International Conference on Data Mining
Machine Learning},
Institution = {Carnegie Mellon University},
Author = {
Paul Komarek, Andrew
Moore
},
Title = {Making Logistic Regression A Core Data Mining Tool With TR-IRLS}
}