This paper proposes a new supervised induction algorithm, IGR, that uses each training instances as a guide of rule induction. IGR learns a set of if-then rules by inducing a pseudo-optimun classification rule for each training instance. IGR weighs the induced rules by using the number of trianing instances covered by them and classifies new instances by majority voting with the weights. Experimental results with twenty datasets in UCI repository show IGR can induce more accurate classification rules than existing learning algorithms such as C4.5, AQ and LazyDT. The experiments also show that IGR does not generate too many rules even if it is applied to large problems