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Min Fang

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3 papers

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EAAI Journal 2024 Journal Article

An adaptive class prototype generation framework for partial label learning

Haixiang Li
Min Fang
Xiao Li
Bo Chen

Partial label learning involves instances with multiple potential labels, of which only one is correct. This process poses challenges when managing limited-size datasets and ambiguity arising from overlapping characteristics across various categories. This paper proposes a novel framework based on class prototype generation to address these issues. The framework integrates a generator that creates label-specific prototypes, a discriminator that assigns weights reflecting the representativeness of examples, and a classifier learning with the assistance of the generator and discriminator. We introduce an adaptive loss adjustment method intended to dynamically balance each model’s weights and mitigate the potential negative impact of inferior examples. Experimental results demonstrate our method’s superiority over state-of-the-art techniques on widely used real-world datasets, with various visualizations illustrating its effectiveness.

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EAAI Journal 2015 Journal Article

Dependence maximization based label space dimension reduction for multi-label classification

Ju-Jie Zhang
Min Fang
Hongchun Wang
Xiao Li

High dimensionality of label space poses crucial challenge to efficient multi-label classification. Therefore, it is needed to reduce the dimensionality of label space. In this paper, we propose a new algorithm, called dependence maximization based label space reduction (DMLR), which maximizes the dependence between feature vectors and code vectors via Hilbert–Schmidt independence criterion while minimizing the encoding loss of labels. Two different kinds of instance kernel are discussed. The global kernel for DMLRG and the local kernel for DMLRL take global information and locality information into consideration respectively. Experimental results over six categorization problems validate the superiority of the proposed algorithm to state-of-art label space dimension reduction methods in improving performance at the cost of a very short time.

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EAAI Journal 2014 Journal Article

Collaborative multi-agent reinforcement learning based on a novel coordination tree frame with dynamic partition

Min Fang
Frans C.A. Groen
Hao Li
Jujie Zhang

In the research of team Markov games, computing the coordinate team dynamically and determining the joint action policy are the main problems. To deal with the first problem, a dynamic team partitioning method is proposed based on a novel coordinate tree frame. We build a coordinate tree with coordinate agent subset and define two breaching weights to represent the weights of an agent to corporate with the agent subset. Each agent chooses the agent subset with a minimum cost as the coordinate team based on coordinate tree. The Q-learning based on belief allocation studies multi-agents joint action policy which helps corporative multi-agents joint action policy to converge to the optimum solution. We perform experiments on multiple simulation environments and compare the proposed algorithm with similar ones. Experimental results show that the proposed algorithms are able to dynamically compute the corporative teams and design the optimum joint action policy for corporative teams.

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