Induction-algorithm-oriented feature elimination considers not only the data and the target concept but also the induction algorithm that will learn the target concept from the data. Because of its very nature, IAOFE is controlled by abundant parameters. This article reports on a study to understand which parameter settings can produce ideal performance from IAOFE. The authors ran comparative studies for various parameter settings and identified effective configurations. Empirical evidence from a large number of data sets demonstrates that IAOFE, with the suggested parameter configurations, can achieve higher predictive accuracy than existing popular feature selection approaches with statistically significant frequencies.