Cumulativity as inductive bias
Hendrik Blockeel, Luc De Raedt · Lirias · 2000
. An important difference in inductive bias between machine learning approaches is whether they assume the effects of different properties on a target variable to be cumulative or not. We believe this difference may have an important influence 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. 1 Introduction Mitchell [16] defines the inductive bias of a machine learning system as the set of assumptions under which the system is guaranteed to return a correct hypothesis. One could also say that inductive bias is a set of conditions such that, if the conditions hold, the system can be expected to perform well, whereas if t...