  Human inertial thinking schemes can be formed through learning, which are
then applied to quickly solve similar problems later. However, when problems
are significantly different, inertial thinking generally presents the solutions
that are definitely imperfect. In such cases, people will apply creative
thinking, such as reverse thinking, to solve problems. Similarly, machine
learning methods also form inertial thinking schemes through learning the
knowledge from a large amount of data. However, when the testing data are
vastly difference, the formed inertial thinking schemes will inevitably
generate errors. This kind of inertial thinking is called illusion inertial
thinking. Because all machine learning methods do not consider illusion
inertial thinking, in this paper we propose a new method that uses reverse
thinking to correct illusion inertial thinking, which increases the
generalization ability of machine learning methods. Experimental results on
benchmark datasets are used to validate the proposed method.
