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Wanfu Gao

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14 papers

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AAAI Conference 2026 Conference Paper

Combining LLM Semantic Reasoning with GNN Structural Modeling for Multi-View Multi-Label Feature Selection

Zhiqi Chen
Yuzhou Liu
Jiarui Liu
Wanfu Gao

Multi-view multi-label feature selection aims to identify informative features from heterogeneous views, where each sample is associated with multiple interdependent labels. This problem is particularly important in machine learning involving high-dimensional, multimodal data such as social media, bioinformatics or recommendation systems. Existing Multi-View Multi-Label Feature Selection (MVMLFS) methods mainly focus on analyzing statistical information of data, but seldom consider semantic information. In this paper, we aim to use these two types of information jointly and propose a method that combines Large Language Models (LLMs) semantic reasoning with Graph Neural Networks (GNNs) structural modeling for MVMLFS. Specifically, the method consists of three main components. (1) LLM is first used as an evaluation agent to assess the latent semantic relevance among feature, view, and label descriptions. (2) A semantic-aware heterogeneous graph with two levels is designed to represent relations among features, views and labels: one is a semantic graph representing semantic relations, and the other is a statistical graph. (3) A lightweight Graph Attention Network (GAT) is applied to learn node embedding in the heterogeneous graph as feature saliency scores for ranking and selection. Experimental results on multiple benchmark datasets demonstrate the superiority of our method over state-of-the-art baselines, and it is still effective when applied to small-scale datasets, showcasing its robustness, flexibility, and generalization ability.

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AAAI Conference 2026 Conference Paper

Redundancy-optimized Multi-head Attention Networks for Multi-view Multi-label Feature Selection

Yuzhou Liu
Jiarui Liu
Wanfu Gao

Multi-view multi-label data offers richer perspectives for artificial intelligence, but simultaneously presents significant challenges for feature selection due to the inherent complexity of interrelations among features, views and labels. Attention mechanisms provide an effective way for analyzing these intricate relationships. They can compute importance weights for information by aggregating correlations between Query and Key matrices to focus on pertinent Values. However, existing attention-based feature selection methods predominantly focus on intra-view relationships, neglecting the complementarity of inter-view features and the critical feature-label correlations. Moreover, they often fail to account for feature redundancy, potentially leading to suboptimal feature subsets. To overcome these limitations, we propose a novel method based on Redundancy-optimized Multi-head Attention Networks for Multi-view Multi-label Feature Selection (RMAN-MMFS). Specifically, we employ each individual attention head to model intra-view feature relationships and use the cross-attention mechanisms between different heads to capture inter-view feature complementarity. Furthermore, we design static and dynamic feature redundancy terms: the static term mitigates redundancy within each view, while the dynamic term explicitly models redundancy between unselected and selected features across the entire selection process, thereby promoting feature compactness. Comprehensive evaluations on six real-world datasets, comparing against six multi-view multi-label feature selection methods, demonstrate the superior performance of the proposed method.

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AAAI Conference 2026 Conference Paper

The Semantic Architect: How FEAML Bridges Structured Data and LLMs for Multi-Label Tasks

Wanfu Gao
Zebin He
Jun Gao

Existing feature engineering methods based on large language models (LLMs) have not yet been applied to multi-label learning tasks. They lack the ability to model complex label dependencies and are not specifically adapted to the characteristics of multi-label tasks. To address the above issues, we propose Feature Engineering Automation for Multi-Label Learning (FEAML), an automated feature engineering method for multi-label classification which leverages the code generation capabilities of LLMs. By utilizing metadata and label co-occurrence matrices, LLMs are guided to understand the relationships between data features and task objectives, based on which high-quality features are generated.The newly generated features are evaluated in terms of model accuracy to assess their effectiveness, while Pearson correlation coefficients are used to detect redundancy. FEAML further incorporates the evaluation results as feedback to drive LLMs to continuously optimize code generation in subsequent iterations. By integrating LLMs with a feedback mechanism, FEAML realizes an efficient, interpretable and self-improving feature engineering paradigm. Empirical results on various multi-label datasets demonstrate that our FEAML outperforms other feature engineering methods.

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IJCAI Conference 2025 Conference Paper

Dual-Agent Reinforcement Learning for Automated Feature Generation

Wanfu Gao
Zengyao Man
Hanlin Pan
Kunpeng Liu

Feature generation involves creating new features from raw data to capture complex relationships among the original features, improving model robustness and machine learning performance. Current methods using reinforcement learning for feature generation have made feature exploration more flexible and efficient. However, several challenges remain: first, during feature expansion, a large number of redundant features are generated. When removing them, current methods only retain the best features each round, neglecting those that perform poorly initially but could improve later. Second, the state representation used by current methods fails to fully capture complex feature relationships. Third, there are significant differences between discrete and continuous features in tabular data, requiring different operations for each type. To address these challenges, we propose a novel dual-agent reinforcement learning method for feature generation. Two agents are designed: the first generates new features, and the second determines whether they should be preserved. A self-attention mechanism enhances state representation, and diverse operations distinguish interactions between discrete and continuous features. The experimental results on multiple datasets demonstrate that the proposed method is effective.

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EAAI Journal 2025 Journal Article

Exploring multi-label feature selection via feature and label information supplementation

Suqi Zhang
Yonghao Li
Ping Zhang
Wanfu Gao

Multi-label feature selection has garnered significant attention for its ability to identify informative features while capturing complex dependencies between labels and features. Therefore, it is essential for dealing with the high dimensionality of multi-label problems. Traditional information-theoretic-based feature selection methods ignore the dynamic nature of the relationship between label relationships and selected features with the labels while assessing candidate features. To address these limitations, we propose a novel method called Feature and Label Information Supplementation (FLIS), which integrates label information supplementation and the mechanisms of selected features with labels. In the FLIS method, we consider dynamic changes in selected features based on conditional mutual information and the mutual information among selected features, candidate features, and labels. Additionally, we distinguish label relationships that provide label information supplementation. Therefore, FLIS can dynamically evaluate the impact of different features on the label relationships. Experimental results on multiple benchmark datasets demonstrate that FLIS captures supplementary information from dynamic relationships between features and labels and significantly improves classification accuracy compared to traditional multi-label feature selection methods.

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IJCAI Conference 2025 Conference Paper

Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method

Wanfu Gao
Jun Gao
Qingqi Han
Hanlin Pan
Kunpeng Liu

The rapid growth in feature dimension may introduce implicit associations between features and labels in multi-label datasets, making the relationships between features and labels increasingly complex. Moreover, existing methods often adopt low-dimensional linear decomposition to explore the associations between features and labels. However, linear decomposition struggles to capture complex nonlinear associations and may lead to misalignment between the feature space and the label space. To address these two critical challenges, we propose innovative solutions. First, we design a random walk graph that integrates feature-feature, label-label, and feature-label relationships to accurately capture nonlinear and implicit indirect associations, while optimizing the latent representations of associations between features and labels after low-rank decomposition. Second, we align the variable spaces by leveraging low-dimensional representation coefficients, while preserving the manifold structure between the original high-dimensional multi-label data and the low-dimensional representation space. Extensive experiments and ablation studies conducted on seven benchmark datasets and three representative datasets using various evaluation metrics demonstrate the superiority of the proposed method.

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IJCAI Conference 2025 Conference Paper

Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning

Wanfu Gao
Hanlin Pan
Qingqi Han
Kunpeng Liu

The "Curse of dimensionality" is prevalent across various data patterns, which increases the risk of model overfitting and leads to a decline in model classification performance. However, few studies have focused on this issue in Partial Multi-label Learning (PML), where each sample is associated with a set of candidate labels, at least one of which is correct. Existing PML methods addressing this problem are mainly based on the low-rank assumption. However, low-rank assumption is difficult to be satisfied in practical situations and may lead to loss of high-dimensional information. Furthermore, we find that existing methods have poor ability to identify positive labels, which is important in real-world scenarios. In this paper, a PML feature selection method is proposed considering two important characteristics of dataset: label relationship's noise-resistance and label connectivity. Our proposed method utilizes label relationship's noise-resistance to disambiguate labels. Then the learning process is designed through the reformed low-rank assumption. Finally, representative labels are found through label connectivity, and the weight matrix is reconstructed to select features with strong identification ability to these labels. The experimental results on benchmark datasets demonstrate the superiority of the proposed method.

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AAAI Conference 2025 Conference Paper

Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection

Hanlin Pan
Kunpeng Liu
Wanfu Gao

The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguation, previous methods mainly focus on utilizing the information inside the labels and the relationship between the labels and features. However, the information existing in the feature space is rarely considered, especially in partial multi-label scenarios where the noises is considered to be concentrated in the label space while the feature information is correct. This paper proposes a method based on latent space alignment, which uses the information mined in feature space to disambiguate in latent space through the structural consistency between labels and features. In addition, previous methods overestimate the consistency of features and labels in the latent space after convergence. We comprehensively consider the similarity of latent space projections to feature space and label space, and propose new feature selection term. This method also significantly improves the positive label identification ability of the selected features. Comprehensive experiments demonstrate the superiority of the proposed method.

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IJCAI Conference 2025 Conference Paper

Two-Stage Feature Generation with Transformer and Reinforcement Learning

Wanfu Gao
Zengyao Man
Zebin He
Yuhao Tang
Jun Gao
Kunpeng Liu

Feature generation is a critical step in machine learning, aiming to enhance model performance by capturing complex relationships within the data and generating meaningful new features. Traditional feature generation methods heavily rely on domain expertise and manual intervention, making the process labor-intensive and challenging to adapt to different scenarios. Although automated feature generation techniques address these issues to some extent, they often face challenges such as feature redundancy, inefficiency in feature space exploration, and limited adaptability to diverse datasets and tasks. To address these problems, we propose a Two-Stage Feature Generation (TSFG) framework, which integrates a Transformer-based encoder-decoder architecture with Proximal Policy Optimization (PPO). The encoder-decoder model in TSFG leverages the Transformer’s self-attention mechanism to efficiently represent and transform features, capturing complex dependencies within the data. PPO further enhances TSFG by dynamically adjusting the feature generation strategy based on task-specific feedback, optimizing the process for improved performance and adaptability. TSFG dynamically generates high-quality feature sets, significantly improving the predictive performance of machine learning models. Experimental results demonstrate that TSFG outperforms existing state-of-the-art methods in terms of feature quality and adaptability.

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AAAI Conference 2025 Conference Paper

Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection

Pingting Hao
Kunpeng Liu
Wanfu Gao

In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the complementary part, which may result in noise due to unclear segmentation. In this paper, we propose a unified model constructed from the perspective of global-view reconstruction. Additionally, while feature selection methods can discern the importance of features, they typically overlook the uncertainty of samples, which is prevalent in realistic scenarios. To address this, we incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Thus, the global-view is reconstructed through the graph structure between samples, sample confidence, and the view relationship. The accurate mapping is established between the reconstructed view and the label matrix. Experimental results demonstrate the superior performance of our method on multi-view datasets.

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AAAI Conference 2025 Conference Paper

Utterance-level Emotion Recognition in Conversation with Conversation-level Supervision

Ximing Li
Yuanchao Dai
Zhiyao Yang
Jinjin Chi
Wanfu Gao
Lin Yuanbo Wu

Emotion Recognition in Conversations (ERC) involves automatically identifying the emotion of each utterance in conversations. The emotion of an utterance is contingent to the conversation context, and thus, annotating each utterance in ERC entails repetitive screening the whole conversation from annotators. Such a requirement leads to prohibitive cost in fine-grained labeling on utterance. In this paper, we propose an efficient coarse-grained labeling strategy for ERC, which assigns a set of emotions for each conversation. In specific, we reformulate the ERC predictors with conversation-level emotion sets as weakly-supervised learning to optimise a potential candidate for ERC, which is termed as Dataless ERC (DERC). To validate this, we propose a simple-yet-flexible DERC framework with Progressive Learning (DERC-PL). We jointly update pseudo-utterance-level emotions and the ERC predictor in a self-training manner, where we progressively update the ERC predictor from training subsets with lower noise densities to the ones with higher noise densities. We implemented several versions of \baby by incorporating various off-the-shelf ERC methods. Extensive experimental results demonstrate that the proposed \baby can be on par with existing weakly-supervised learning baselines and supervised learning ERC methods.

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AAAI Conference 2024 Conference Paper

Double-Layer Hybrid-Label Identification Feature Selection for Multi-View Multi-Label Learning

Pingting Hao
Kunpeng Liu
Wanfu Gao

Multi-view multi-label feature selection aims to select informative features where the data are collected from multiple sources with multiple interdependent class labels. For fully exploiting multi-view information, most prior works mainly focus on the common part in the ideal circumstance. However, the inconsistent part hidden in each view, including noises and specific elements, may affect the quality of mapping between labels and feature representations. Meanwhile, ignoring the specific part might lead to a suboptimal result, as each label is supposed to possess specific characteristics of its own. To deal with the double problems in multi-view multi-label feature selection, we propose a unified loss function which is a totally splitting structure for observed labels as hybrid labels that is, common labels, view-to-all specific labels and noisy labels, and the view-to-all specific labels further splits into several specific labels of each view. The proposed method simultaneously considers the consistency and complementarity of different views. Through exploring the feature weights of hybrid labels, the mapping relationships between labels and features can be established sequentially based on their attributes. Additionally, the interrelatedness among hybrid labels is also investigated and injected into the function. Specific to the specific labels of each view, we construct the novel regularization paradigm incorporating logic operations. Finally, the convergence of the result is proved after applying the multiplicative update rules. Experiments on six datasets demonstrate the effectiveness and superiority of our method compared with the state-of-the-art methods.

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IJCAI Conference 2024 Conference Paper

TFWT: Tabular Feature Weighting with Transformer

Xinhao Zhang
Zaitian Wang
Lu Jiang
Wanfu Gao
Pengfei Wang
Kunpeng Liu

In this paper, we propose a novel feature weighting method to address the limitation of existing feature processing methods for tabular data. Typically the existing methods assume equal importance across all samples and features in one dataset. This simplified processing methods overlook the unique contributions of each feature, and thus may miss important feature information. As a result, it leads to suboptimal performance in complex datasets with rich features. To address this problem, we introduce Tabular Feature Weighting with Transformer, a novel feature weighting approach for tabular data. Our method adopts Transformer to capture complex feature dependencies and contextually assign appropriate weights to discrete and continuous features. Besides, we employ a reinforcement learning strategy to further fine-tune the weighting process. Our extensive experimental results across various real-world datasets and diverse downstream tasks show the effectiveness of TFWT and highlight the potential for enhancing feature weighting in tabular data analysis.

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EAAI Journal 2021 Journal Article

A conditional-weight joint relevance metric for feature relevancy term

Ping Zhang
Wanfu Gao
Juncheng Hu
Yonghao Li

Feature selection is an important preprocessing operation in the fields of machine learning and data mining. Information theory is widely used in feature selection methods because it can measure linear and nonlinear correlations among variables. Traditional information theory-based feature selection methods intend to maximize feature relevancy while minimizing feature redundancy. However, previous feature selection methods focus on either the effect of candidate features or the effect of already-selected features on the feature relevancy. In fact, both candidate features and already-selected features offer important classification information in the design of feature relevancy term. To avoid this problem, we extract useful classification information from joint mutual information to design a novel feature relevancy term named Conditional-Weight Joint Relevance (CWJR). Based on CWJR, we propose a novel feature selection method named Feature Selection considering Conditional-Weight Joint Relevance (CWJR-FS). Additionally, to distinguish the differences between our method and previous methods, we divide information theory-based feature selection methods into two categories: linear-based feature selection methods and nonlinear-based feature selection methods. Finally, our method is compared to seven linear-based methods and four nonlinear-based methods on 19 benchmark data sets. The experimental results demonstrate that CWJR-FS outperforms the compared methods in terms of the average classification accuracy, AUC and F1 score.

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