DECISION TREE INDUCTION FROM COUNTEREXAMPLES
Nicolas Cebron, Fabian Richter, Rainer W. Lienhart · 2012
While it is well accepted in human learning to learn from counterexamples or mistakes, classic machine learning algorithms still focus only on correctly labeled training examples.We replace this rigid paradigm by using complementary probabilities to describe the probability that a certain class does not occur.Based on the complementary probabilities, we design a decision tree algorithm that learns from counterexamples.In a classification problem with K classes, K -1 counterexamples correspond to one correctly labeled training example.We demonstrate that even when only a partial amount of counterexamples is available, we can still obtain good performance.