Modeling reverse thinking for machine learning

TL;DR

This paper introduces a novel reverse thinking approach to correct illusion inertial thinking in machine learning, enhancing its ability to generalize across vastly different datasets.

Contribution

It proposes a new method that applies reverse thinking to improve machine learning generalization, addressing the issue of illusion inertial thinking.

Findings

Improved accuracy on benchmark datasets

Enhanced generalization ability of machine learning models

Validation of the method's effectiveness through experiments

Abstract

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.