This repository contains an implementation of the ITER and GridEx algorithms and useful code to test these procedures on different data sets.

Class for modeling a data set. This class also splits the whole data set into the training and test sets.

An implementation of the ITER algorithm.

An implementation of the GridEx Algorithm.

Class for training a neural network solving regression tasks.

A collection of useful functions.

The algorithms can be simply tested on a collection of predefined data sets (for more details see the References section).

With the following instrucion the functions for reproducing the experiments are loaded:

from utils import *

To split the data sets into train and test sets and to build and train artificial neural network predictors, this instruction can be used:

trainAndSave()

The following instruction applies ITER to all the available data sets and displays the results:

testIter()

Finally, the same can be executed for GridEx with this instruction:

testGridex()

1. Airfoil Self-Noise Data Set. https://archive.ics.uci.edu/ml/datasets/Airfoil+Self-Noise (2014), [Online; last accessed 19 Jan 2021].
2. Combined Cycle Power Plant Data Set. https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant (2014), [Online; last accessed 19 Jan 2021].
3. Energy Efficiency Data Set. https://archive.ics.uci.edu/ml/datasets/Energy+efficiency (2012), [Online; last accessed 19 Jan 2021].
4. Gas Turbine CO and NOx Emission Data Set. https://archive.ics.uci.edu/ml/datasets/Gas+Turbine+CO+and+NOx+Emission+Data+Set (2019), [Online; last accessed 19 Jan 2021].
5. Huysmans, J., Baesens, B., Vanthienen, J.: Iter: an algorithm for predictive regression rule extraction. In: International Conference on Data Warehousing and Knowledge Discovery. pp. 270{279. Springer (2006).
6. Wine Quality Data Set. https://archive.ics.uci.edu/ml/datasets/Wine+Quality (2009), [Online; last accessed 19 Jan 2021].