Accelerated Higher Order Logistic Regression
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README.md

README.md

ALR

This is the code for Accelerated Higher Order Logistic Regression.

Underlying research and scientific papers

This code is supporting the following research paper:

ALRn: accelerated higher-order logistic regression, Zaidi, N.A., Webb, G.I., Carman, M.J., Petitjean, F. and Cerquides, J. Machine Learning (2016). doi:10.1007/s10994-016-5574-8.

When using this repository, please cite:

@article{Zaidi2016,
  title={ALRn: accelerated higher-order logistic regression},
  author={Zaidi, Nayyar A and Webb, Geoffrey I and Carman, Mark J and Petitjean, Fran{\c{c}}ois and Cerquides, Jes{\'u}s},
  journal={Machine Learning},
  volume={104},
  number={2-3},
  pages={151--194},
  year={2016},
  publisher={Springer}
}

An Example command line of the code:

java ALR.BVDcrossvalx -t /Users/nayyar/WData/datasets_DM/nursery.arff -i 2 -x 2 -W ALR.wdAnJE -- -S "A1JE" -P "dCCBN" -I "Flat" -O "GD"

BVDcrossvalx does 'i' rounds of 'x' fold cross validation and calls ALR classifier.

ALR takes in following arguments:

-S A1JE, A2JE, A3JE, A4JE, A5JE

This specifies to use linear, quadratic, cubic, quartic or quintic features

-P MAP, dCCBN, wCCBN

This specifies to use either generative MAP learning or LR or ALR

-I Flat, Indexed, IndexedBig, BitMap

This depends on the data and its dimensions and implements the model effectively