arXiv subjects

Fangqing Liu

Publications and source records attributed to Fangqing Liu.

2 recordsLinked to original sources

arXiv2025

Regionalized Metric Framework: A Novel Approach for Evaluating Multimodal Multi-Objective Optimization Algorithms

This study aims to optimize the evaluation metric of multimodal multi-objective optimization problems using a Regionalized Metric Framework, which provides a certain boost to research in this field. Existing evaluation metrics usually use the reference set as the evaluation basis, which inevitably leads to reference set dependence. To optimize this problem, this study proposes an evaluation metric based on a Regionalized Metric Framework. The algorithm divides the set of solutions to be evaluated into three regions, and evaluates each solution according to a unique scoring function for each region, which is combined to form the evaluation value of the solution set. To verify the feasibility of this method, a comparative experiment was conducted in this study. The results of the experiment are roughly the same as the trend of existing indicators, and at the same time, it can accurately judge the advantages and disadvantages of points equidistant from the reference set. Our method provides a new perspective for further research on evaluation metrics for multimodal multi-objective optimization algorithms.

cs.NE↗

arXiv2020

A Variational Approach for Learning from Positive and Unlabeled Data

Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally. Most recent PU learning methods are developed based on the conventional misclassification risk of the supervised learning type, and they require to solve the intractable risk estimation problem by approximating the negative data distribution or the class prior. In this paper, we introduce a variational principle for PU learning that allows us to quantitatively evaluate the modeling error of the Bayesian classifier directly from given data. This leads to a loss function which can be efficiently calculated without any intermediate step or model, and a variational learning method can then be employed to optimize the classifier under general conditions. In addition, the discriminative performance and numerical stability of the variational PU learning method can be further improved by incorporating a margin maximizing loss function. We illustrate the effectiveness of the proposed variational method on a number of benchmark examples.

cs.LG↗