Cost-sensitive stacking integrated learning framework based on feature inverse mapping

Abstract

The invention provides a cost-sensitive stacking integrated learning framework based on feature inverse mapping in order to effectively solve the problem of unbalanced classification. Firstly, a random forest, a limit forest, a gradient tree, linear discriminant analysis and logistic regression are simultaneously adopted for training of data sets as basic classifiers; then confidences obtained bycross validation of the basic classifiers are stacked through a stacking integrated learning method to form a new feature set; feature exponential transform of the new feature set is performed, an exponent shown in the description of the optimal average logarithmic loss is selected, and feature inverse mapping of the feature is performed with the exponent shown in the description; and finally, thefeature set after inverse mapping is trained by employing cost-sensitive logistic regression. In test steps, the feature obtained by stacking avoids the operation of inverse mapping. Compared with aconventional unbalanced classification integration method, according to the cost-sensitive stacking integrated learning framework, cost sensitivity and stacking integration are firstly combined so that the generalization performance of the unbalanced classification problem is effectively enhanced, and a model can obtain a stable classification threshold.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to an integrated model for imbalanced classification problems. Background technique [0002] Pattern recognition is the study of using computers to imitate or realize the recognition ability of humans or other animals in order to complete the task of automatic recognition of research objects. In recent years, pattern recognition technology has been widely used in many important fields such as artificial intelligence, machine learning, computer engineering, robotics, neurobiology, medicine, detective science, archaeology, geological exploration, aerospace science and weapon technology. In the past decade, the imbalance problem has received extensive attention in the field of pattern recognition. Because most of the data obtained in real life is unbalanced, such as medical data, access control data, email data and so on. However, the imbalance problem is a huge challenge...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the fact that existing ensemble algorithms cannot effectively solve the imbalance problem, the present invention proposes a cost-sensitive stack ensemble learning framework based on feature inverse mapping by combining stack integration and cost sensitivity

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