Author name cluster

Dexin Ren

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

EAAI Journal 2025 Journal Article

A new adaptive robust multi-objective optimization algorithm for dispatching of microgrids design

Xue Feng
Lei Cui
Dexin Ren
Anqi Pan
Juchen Hong

Uncertainty widely exists in real-world applications. When uncertainty occurs, the robustness of solutions obtained by optimization is critical to the operation of the application. In the process of solving robust solutions, the balance between robustness and convergence is the key problem. Attaching undue importance to robustness leads to local optimality, while excessive convergence leads to loss of robust solutions. Motivated by above, a new adaptive robust multi-objective optimization algorithm is proposed. This algorithm proposes both a convergence-driven strategy and a robustness-driven strategy, and it adaptively selects between them by evaluating the population's evolutionary state. In the robust driven strategy, a new robustness metric is proposed to evaluate the robustness of individuals under disturbance. A penalty function based on the proposed metric guides the environmental selection process to enhance robustness. In the convergent driven strategy, the environment selection is mainly driven by the non-dominant relationship. A region robustness estimation strategy is proposed to evaluate the individuals in critical layer. Compared to four state of-the-art multi-objective algorithms on benchmark suits, the proposed algorithm performs better than its peers. In addition, the robust optimization problem of the dispatching of microgrids is constructed and the proposed algorithm is used for optimization of microgrids. The effectiveness of experimental results reveal that our methods are very promising in tackling the dispatching of microgrids problems.

Details DOI

IJCAI Conference 2019 Conference Paper

Positive and Unlabeled Learning with Label Disambiguation

Chuang Zhang
Dexin Ren
Tongliang Liu
Jian Yang
Chen Gong

Positive and Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled training data. The state-of-the-art methods usually formulate PU learning as a cost-sensitive learning problem, in which every unlabeled example is simultaneously treated as positive and negative with different class weights. However, the ground-truth label of an unlabeled example should be unique, so the existing models inadvertently introduce the label noise which may lead to the biased classifier and deteriorated performance. To solve this problem, this paper proposes a novel algorithm dubbed as "Positive and Unlabeled learning with Label Disambiguation'' (PULD). We first regard all the unlabeled examples in PU learning as ambiguously labeled as positive and negative, and then employ the margin-based label disambiguation strategy, which enlarges the margin of classifier response between the most likely label and the less likely one, to find the unique ground-truth label of each unlabeled example. Theoretically, we derive the generalization error bound of the proposed method by analyzing its Rademacher complexity. Experimentally, we conduct intensive experiments on both benchmark and real-world datasets, and the results clearly demonstrate the superiority of the proposed PULD to the existing PU learning approaches.

PDF Details