Modeling reverse thinking for machine learning
This addresses a fundamental limitation in machine learning generalization for applications where test data differs substantially from training data, though it appears incremental as it builds on existing concepts of inertial thinking.
The paper tackles the problem of 'illusion inertial thinking' in machine learning, where models trained on large datasets fail on significantly different test data, by proposing a reverse thinking method to correct this issue and improve generalization, with experimental validation on benchmark datasets.
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.