Abstract

In many domains, an appropriate inductive bias is the MIN-FEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This paper defines and studies this bias in Boolean domains. First, it is shown that any learning algorithm implementing the MIN-FEATURES bias requires \Theta( 1 ffl ln 1 ffi + 1 ffl [2 p + p ln n]) training examples to guarantee PAC-learning a concept having p relevant features out of n available features. This bound is only logarithmic in the number of irrelevant features. For implementing the MIN-FEATURES bias, the paper presents five algorithms that identify a subset of features sufficient to construct a hypothesis consistent with the training examples. FOCUS-1 is a straightforward algorithm that returns a minimal and sufficient subset of features in quasi-polynomial time. FOCUS-2 does the same task as FOCUS-1 but is empirically shown to be substantially faster than FOCUS-1. Finally, the Simple-Greedy, Mutual-Information-G...

Citations