Cumulativity as Inductive Bias

An important di erence in inductive bias between machine learning approaches is whether they assume the e ects of di erent properties on a target variable to be cumulative or not. We believe this di erence may have an important in uence on the performance of machine learning or data mining techniques, and hence should be taken into account when deciding which techniques to use. We illustrate this point with some practical cases. We furthermore point out that in Inductive Logic Programming, most algoritms belong to the class that does not assume cumulativity. We argue for the use and/or development of ILP systems that do make this assumption.