An inverse classification framework with limited budget and maximum number of perturbed samples.

Jaehoon Koo Diego Klabjan Jean Utke

Published in: Expert Syst. Appl. (2023)

Keyphrases

maximum number
limited budget
data sets
decision trees
training set
feature vectors
supervised learning
training samples
support vector
genetic algorithm
feature extraction
classification accuracy
non stationary
class labels