Standard empirical learners that use domain knowledge require stronger
knowledge that is hard and expensive to acquire. However, weaker domain
knowledge can benefit from prior knowledge while being cost effective. Weak
knowledge in the form of feature relative importance (FRI) is presented and
explained. Feature relative importance is a real valued approximation of a
feature's importance provided by experts. Advantage of using this knowledge is
demonstrated by IANN, a modified multilayer neural network algorithm.