Guihua Wen

South China University of Technology

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i-10 index

Research Trends

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Co-Authors

Yang Hu

University of Oxford

Lijun Jiang

University of Electronic Science and Technology of China

Huihui Li

Tsinghua University

Pei Yang

Yantai University

Zhiwen Yu

South China University of Technology

Mingnan Luo

South China University of Technology

Jun Wen

Guangxi University

Dan Dai

Zhejiang A & F University

Jia Wei

South China University of Technology

Changjun Wang

Guangdong Academy of Medical Sciences

Cooperative Institutions

South China University of Technology

Chinese Academy of Sciences

Sun Yat-sen University

Huazhong University of Science and Technology

Guangdong Polytechnic Normal University

Tsinghua University

Beijing Institute of Technology

The First Affiliated Hospital, Sun Yat-sen University

Jinan University

Northwestern Polytechnical University

Author Statistics

Papers

Citation

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i-10 index

Research Field

Cognitive gravitation model-based relative transformation for classification

Soft Computing (2016)

Yaxin Sun Guihua Wen

Benchmark (surveying)

10.1007/s00500-016-2131-0

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Citations (3)

Perceptual Relativitybased L ocal Mean Center Classifier

DEStech Transactions on Engineering and Technology Research (2017)

Xianfa Cai Guihua Wen Jia Wei Zhiwen Yu Yongming Cai

1Despite k -nearest neighbors and its variants perform very well in many applications, they usually suffer from such limitations as sensitivity to noisy, sparse and imbalance data which results to a dramatic performance degradation. However human cognition has its unique ability to deal with these issues from the different perspective. Motivated by this character, this paper proposes a feasible strategy called Perceptual Relativity-based Local Mean Center Classifier by using the relative transformation to local mean center classifier (RLMC). Firstly, relative transformation will be performed over the training samples to build the relative space and find k nearest neighbors in the relative space. The advantage of relative transformation is that it improves the distinguishing ability among data points and diminishes the impact of noise on classification. Experimental results on both real and simulated data suggest that the proposed approach often gives the better results in classification and robustness.

Robustness

10.12783/dtetr/apetc2017/11274

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Citations (0)

Semi-supervised dimensionality reduction based on local estimation error

International Journal of High Performance Computing and Networking (2019)

Xianfa Cai Jia Wei Guihua Wen Zhiwen Yu Yongming Cai

The construction of a graph is extremely important in graph-based semi-supervised learning. However, it is unstable by virtue of sensitivity to the selection of neighbourhood parameter and inaccuracy of the edge weights. Inspired by the good performance of the local learning method, this paper proposes a semi-supervised dimensionality reduction based on local estimation error (LEESSDR) algorithm by utilising local learning projections (LLP) to semi-supervised dimensionality reduction. The algorithm sets the edge weights through minimising the local estimation error and can effectively preserve the global geometric structure as well as the local one of the data. Since LLP does not require its input space to be locally linear, even if it is nonlinear, LLP maps it to the feature space by using kernel functions and then obtains its local estimation error in the feature space. The effectiveness of the proposed method is verified on two popular face databases with promising classification accuracy and favourable robustness.

Robustness

Neighbourhood (mathematics)

Feature vector

10.1504/ijhpcn.2019.099745

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Citations (1)

Global distilling framework with cognitive gravitation for multimodal emotion recognition

Neurocomputing (2025)

Huihui Li Haoyang Zhong Chunlin Xu Xiaoyong Liu Guihua Wen

10.1016/j.neucom.2024.129306

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Citations (0)

From Table to Image: Boosting Credit Risk Prediction via Transfer MLP-like Network on Structured Data

Research Square (Research Square) (2024)

Yan Li Guihua Wen Bo Liu

Abstract At present, deep learning has limited application in the field of financial credit risk because deep learning is good at processing unstructured data such as images, voice, and text, while the credit risk field processes structured tabular data, which makes the existing deep learning methods not well adapted to financial structured data tasks. To this end, this paper proposes a new Table-to-Image Converted Transfer MLP-like network for financial credit risk prediction. First, our method attempts to represent structured data from a new perspective and proposes a data homology based table-to-image conversion method to convert the tabular financial credit risk prediction data into image-like financial data. Then, based on the Strip-MLP structure, a pretrained MLP-like network is proposed to be applied to the credit prediction of the converted image-like financial data. The model is pre-trained with a public financial dataset, and its pre-trained parameters are transferred to the private dataset of financial institutions with different feature numbers and feature contents through transfer learning. Experimental results show that for the task of financial credit risk prediction, the methods proposed in this paper have significantly improved the effect compared with the baseline algorithm.

Boosting

Table (database)

Gradient boosting

10.21203/rs.3.rs-4795897/v1

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Citations (0)

A Neighborhood Preserving Based Semi-supervised Dimensionality Reduction Method for Cancer Classification

Advances in intelligent and soft computing (2011)

Xianfa Cai Jia Wei Guihua Wen Jie Li

Feature (linguistics)

Supervised Learning

10.1007/978-3-642-25664-6_4

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Citations (0)

What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data

ACM Transactions on Intelligent Systems and Technology (2022)

Yang Hu Adriane Chapman Guihua Wen Dame Wendy Hall

Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include heavy reliance on massive training data, limited generalizability, and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.

One shot

Representation

10.1145/3510030

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Citations (16)

Learning features from irrelevant domains through deep neural network

Complex & Intelligent Systems (2023)

Pengcheng Wen Yuhan Zhang Guihua Wen

Abstract Features of data are much critical to the classification. However, when only small data are available, suitable features can not be easily obtained, easily leading to the bad classification performance. This paper propose a novel approach to automatically learns features from the irrelevant domain with much discriminative features for the given classification task. It first computes as the learning objectives the central vectors of each class in the irrelevant domain, and then uses machine learning method to automatically learn features for each sample in the target domain from these objectives. The merits of our method lie in that unlike the transfer learning, our method does not require the similarity between two domains. It can learn features from much discriminative domains. Its learned features are not limited to its original ones, unlike feature selection and feature extraction methods, so that the classification performance with the learned features can be better. Finally, our method is much general, simple, and efficient. Lots of experimental results validated the proposed method.

Discriminative model

Similarity (geometry)

Transfer of learning

Feature (linguistics)

10.1007/s40747-023-01157-6

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Multi-label body constitution recognition via HWmixer-MLP for facial and tongue images

Expert Systems with Applications (2025)

Mengjian Zhang Guihua Wen Pei Yang Changjun Wang Chuyun Chen

10.1016/j.eswa.2025.126383

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A self-optimizing approach for knowledge acquisition with adaptively incremental sampling

Dan Pan Qi-Lun Zheng Jing-Song Hu Guihua Wen

The paper outlines a self-optimizing approach for knowledge acquisition with adaptively incremental sampling, which fused the self-optimizing approach for knowledge acquisition and sampling approaches in order to improve the efficiency of knowledge acquisition effectively. The proposed sampling approach enabled us to dynamically and adaptively adjust the sample size according to the data mining algorithm's performance on the training samples so as to utilize the sample size as small as possible without reducing the accuracy of the knowledge model. Finally, the self-optimizing approach for knowledge acquisition with adaptively incremental sampling was applied to rule generation from the diagnostic decision table for rheumatoid arthritis in Chinese medical science. Experimentation results showed that the approach was much better than other algorithms both in efficiency and in accuracy.

Knowledge Acquisition

Sample (material)

10.1109/icsmc.2001.972950

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