Local Distortion Hiding in Financial Technology application: a case study with a benchmark data set (IISA 2019)

The 10th International Conference on Information, Intelligence, Systems and Applications 15-17 July 2019

Data Files

German Credit Data Set *
 Original German Credit data set
Original German Credit data set:weka.filters.supervised.attribute.NominalToBinary
Original Binarized German Credit data set
Original Binarized German Credit data set: WEKA model
Original Binarized German Credit Decision Tree
Modified Binarized German Credit data set
Original Binarized German Credit data set: WEKA model
Modified Binarized German Credit Decision Tree

*Statlog (German Credit Data)

Knowledge Hiding in Decision Trees for Learning Analytics Applications - Advances in Core Computer Science-Based Technologies pp 37-54(Springer)

Data Files

Student Performance Data Set *
 Original Student Performance Data Set:weka.filters.supervised.attribute.NominalToBinary
Original Binarized Student Performance Data Set: WEKA model
Original Binarized Student Performance Data Set Decision Tree_1st Example
Modified Binarized Student Performance Data Set_1st Example
Modified Binarized Student Performance Data Set: WEKA model_1st Example
Modified Binarized Student Performance Decision Tree_1st Example
Original Binarized Student Performance Data Set Decision Tree_2nd Example
Modified Binarized Student Performance Data Set_2nd Example
Modified Binarized Student Performance Data Set: WEKA model_2nd Example
Modified Binarized Student Performance Decision Tree_2nd Example



*Student Performance Data Set

Weka 3: Data Mining Software in Java