Semi-supervised Learning Method Research Articles

Customization of latent space in semi-supervised Variational AutoEncoder

We propose a novel semi-supervised learning method of Variational AutoEncoder (VAE), which yields a customized latent space through our EXplainable encoder Network (EXoN). The customization involves a manual design of the interpolation and structural constraint, such as proximity, which enhances the interpretability of the latent space. To improve the classification performance, we introduce a new semi-supervised classification method called SCI (Soft-label Consistency Interpolation). Combining the classification loss and the Kullback–Leibler divergence is crucial in constructing an explainable latent space. Additionally, the variability of the generated samples is determined by an active latent subspace, which effectively captures distinctive characteristics. We conduct experiments using the MNIST, SVHN, and CIFAR-10 datasets, and the results demonstrate that our approach yields an explainable latent space while significantly reducing the effort required to analyze representation patterns within the latent space.

EPCO-34. IMAGE-BASED PHENOTYPIC HIGH-CONTENT CRISPR-CAS9 SCREEN FOR CHARACTERIZING CANCER DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA

Abstract The genomic landscape of pediatric high-grade glioma is characterized by a high level of aneuploidy but few focal amplifications or homozygous deletions. Cancer aneuploidies are the principal source of tumor heterogeneity driving tumor growth and therapy resistance. Here, we deployed an image-based phenotypic high-content CRISPR-Cas9 screen to identify aneuploidy-associated pediatric glioma driver genes. Through integrated computational analyses combining whole-genome sequencing datasets from 106 pediatric gliomas and 81 canine gliomas, we identified a set of 460 genes present in regions of aneuploidy in both diseases. We designed a custom guideRNA library to systemically knockout the 460 gene set using an arrayed CRISPR-Cas9 knockout screen in two pediatric high-grade glioma cell lines. By deploying 72 of 384-well plates on automated confocal Opera Phenix High-Content Screening System and dyes staining cell membrane, nucleus, and apoptotic cells, we established 5 million live-cell images across three timepoints (day 7, 8, 9 post-transduction). Each gene was randomized to 32 randomized wells across four 384-well plates to gather robust morphological profiles. Through image-based nucleus and cell segmentation workflow, we characterized 50 million single cells across 27,648 wells over three time points. Using semi-supervised machine learning methods, we derived 1,050 cell morphological features to characterize knockout phenotypes, e.g., viability, growth, and morphology (size, shape, texture of nucleus and cells) across cells. The top ranked genes were then linked to oncogenes and tumor suppressors based on pathway and ontology analysis as well as functional in vitro and in silico validation using Pediatric Cancer Dependency Map. We find convergence of the most impactful molecular abnormalities - based on their knockout phenotypes - on candidate signaling pathways for the development of new drugs and repurposing of existing drugs for pediatric high-grade glioma.

SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification

Medical image classification presents significant challenges due to limited labeled samples and class imbalance resulting from varying disease prevalence. In many real-world scenarios, a scarcity of labeled images pertaining to specific ailments coexists with an abundance of unlabeled ones. The process of annotating these unlabeled medical images by domain experts is both time-intensive and financially burdensome. Consequently, medical image classification remains an active and evolving research area. Semi-supervised learning (SSL) techniques hold promise in addressing these challenges by effectively leveraging both labeled and unlabeled data. However, the application of SSL to medical image classification necessitates overcoming two primary obstacles: (1) the reliable estimation of pseudo-labels for unlabeled images and (2) the mitigation of biases stemming from class imbalance. In this work, we introduce a novel semi-supervised learning approach, denoted as SPLAL, which adeptly addresses these challenges. SPLAL leverages the concept of class prototypes and employs a weighted combination of classifiers to predict dependable pseudo-labels for a subset of unlabeled medical images. Additionally, we introduce an alignment loss term designed to alleviate model biases, particularly those arising from the overrepresentation of majority classes. This alignment loss mechanism ensures the model’s consistency in delivering the same classification output for diverse augmented images of the same disease. To evaluate the effectiveness of our proposed approach, we conducted comprehensive experiments on two publicly accessible benchmark datasets for medical image classification. Our empirical findings unequivocally demonstrate that SPLAL outperforms several state-of-the-art semi-supervised learning methods across various evaluation metrics.

ConvNeXt based semi-supervised approach with consistency regularization for weeds classification

Weed recognition is an essential step for automatic weed control systems. Identifying weeds enables targeted control measures to be implemented, minimizing the use of chemicals and reducing the impact on the environment. Deep learning-based approaches proved to be effective for addressing various complex classification problems. However, to benefit fully from their capabilities, large amounts of labeled data are required, which represents a limitation for agricultural applications, consequence of the tedious and time-consuming process of data labeling. Conversely, unlabeled data could be acquired in large quantities, with relative ease. Hence, our aim is to develop robust and precise deep learning models, to carry-out the recognition and identification of weed species, using both types of data. To this end, we propose a method, that adopts the semi-supervised learning paradigm, to optimally combine labeled and unlabeled data. The method is based on a new deep neural networks architecture, which consists of a modernized convolutional encoder belonging to the family ConvNeXt and a thoroughly designed deep decoder network. This architecture, enables a successful integration of consistency regularization. The conducted experiments on DeepWeeds and 4-Weeds, showed that the semi-supervised models trained through our proposed method provide a stable and high classification performance, compared to other state-of-the-art deep learning models, which were affected negatively by the amount of labeled data available, and the presence of noise during inference. Furthermore, the effectiveness of the proposed method was demonstrated in comparison to other semi-supervised learning methods. The results obtained demonstrate the benefits of adopting the semi-supervised learning paradigm, especially in scenarios with very limited labeled data.

Joint loss learning-enabled semi-supervised autoencoder for bearing fault diagnosis under limited labeled vibration signals

Rolling bearing is a critical component of machinery that has been widely applied in manufacturing, transportation, aerospace, and power and energy industries. The timely and accurate bearing fault detection thus is of vital importance. Computational data-driven deep learning has recently become a prevailing approach for bearing fault detection. Despite the progress of the deep learning approach, the deep learning performance is hinged upon the size of labeled data, the acquisition of which is expensive in actual implementation. Unlabeled data, on the other hand, are inexpensive. In this research, we develop a new semi-supervised learning method built upon the autoencoder to fully utilize a large amount of unlabeled data together with limited labeled data to enhance fault detection performance. Compared with the state-of-the-art semi-supervised learning methods, this proposed method can be more conveniently implemented with fewer hyperparameters to be tuned. In this method, a joint loss is established to account for the effects of labeled and unlabeled data, which is subsequently used to direct the backpropagation training. Systematic case studies using the Case Western Reserve University (CWRU) rolling bearing dataset are carried out, in which the effectiveness of this new method is verified by comparing it with other well-established baseline methods. Specifically, nearly all emulation runs using the proposed methodology can lead to around 2%–5% accuracy increase, indicating its robustness in performance enhancement.

Mapping user behaviors to identify professional accounts in Ethereum using semi-supervised learning

Ethereum is one of the largest blockchain platforms currently that has become a digital business environment. This platform allows for decentralized transactions between anonymous users. Thus, the development of methods to identify users’ behaviors and keep them anonymous can potentially leverage business on this platform. In this work, we aim to combine different categories of machine learning approaches, namely, unsupervised and semi-supervised, to map the behaviors of users’ owned accounts and identify users with professional activities in Ethereum. In addition, we provide here data to the community and analyze different machine learning techniques to characterize the users of Ethereum. These are challenging tasks due to the small fraction of publicly labeled data referring to users’ accounts that provide services on this platform, such as exchange, payment, and entertainment, among most casual behavior users. Initially, we use unsupervised learning techniques to cluster the unlabeled users’ accounts and to identify a set of them with casual behavior. As an outcome, a dataset containing labeled (casual or professional) and unlabeled instances is obtained. Semi-supervised learning methods are then applied (i) to generate models that classify accounts’ behaviors into casual or professional ones and (ii) to discover accounts with professional behaviors among the unlabeled ones. Computational experiments were conducted, and the results obtained by the proposed procedure are compared to those achieved by supervised learning techniques from the literature. The proposal outperformed those from the literature and reached values higher than 95% for the accuracy, precision, recall, Fβ-scores, MCC, and AUC-ROC.

An Integrated Semi-supervised Software Defect Prediction Model

<p>A novel semi-supervised software defect prediction model FFeSSTri (Filtered Feature Selecting, Sample and Tri-training) is proposed to address the problem that class imbalance and too many irrelevant or redundant features in labelled samples lower the accuracy of semi-supervised software defect prediction. Its innovation lies in that the construction of FFeSSTri integrates an oversampling technique, a new feature selection method, and a Tri-training algorithm, thus it can effectively improve the accuracy. Firstly, the oversampling technique is applied to expand the class of inadequate samples, thus it solves the unbalanced classification of the labelled samples. Secondly, a new filtered feature selection method based on relevance and redundancy is proposed, which can exclude those irrelevant or redundant features from labelled samples. Finally, the Tri-training algorithm is used to learn the labelled training samples to build the defect prediction model FFeSSTri. The experiments conducted on the NASA software defect prediction dataset show that FFeSSTri outperforms the existing four supervised learning methods and one semi-supervised learning method in terms of F-Measure values and AUC values.</p> <p> </p>

Self-Paced Co-Training of Graph Neural Networks for Semi-Supervised Node Classification.

Graph neural networks (GNNs) have demonstrated great success in many graph data-based applications. The impressive behavior of GNNs typically relies on the availability of a sufficient amount of labeled data for model training. However, in practice, obtaining a large number of annotations is prohibitively labor-intensive and even impossible. Co-training is a popular semi-supervised learning (SSL) paradigm, which trains multiple models based on a common training set while augmenting the limited amount of labeled data used for training each model via the pseudolabeled data generated from the prediction results of other models. Most of the existing co-training works do not control the quality of pseudolabeled data when using them. Therefore, the inaccurate pseudolabels generated by immature models in the early stage of the training process are likely to cause noticeable errors when they are used for augmenting the training data for other models. To address this issue, we propose a self-paced co-training for the GNN (SPC-GNN) framework for semi-supervised node classification. This framework trains multiple GNNs with the same or different structures on different representations of the same training data. Each GNN carries out SSL by using both the originally available labeled data and the augmented pseudolabeled data generated from other GNNs. To control the quality of pseudolabels, a self-paced label augmentation strategy is designed to make the pseudolabels generated at a higher confidence level to be utilized earlier during training such that the negative impact of inaccurate pseudolabels on training data augmentation, and accordingly, the subsequent training process can be mitigated. Finally, each of the trained GNN is evaluated on a validation set, and the best-performing one is chosen as the output. To improve the training effectiveness of the framework, we devise a pretraining followed by a two-step optimization scheme to train GNNs. Experimental results on the node classification task demonstrate that the proposed framework achieves significant improvement over the state-of-the-art SSL methods.

Graph-Based Semi-Supervised Learning: A Comprehensive Review.

Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of both labeled and unlabelled data. An essential class of SSL methods, referred to as graph-based semi-supervised learning (GSSL) methods in the literature, is to first represent each sample as a node in an affinity graph, and then, the label information of unlabeled samples can be inferred based on the structure of the constructed graph. GSSL methods have demonstrated their advantages in various domains due to their uniqueness of structure, the universality of applications, and their scalability to large-scale data. Focusing on GSSL methods only, this work aims to provide both researchers and practitioners with a solid and systematic understanding of relevant advances as well as the underlying connections among them. The concentration on one class of SSL makes this article distinct from recent surveys that cover a more general and broader picture of SSL methods yet often neglect the fundamental understanding of GSSL methods. In particular, a significant contribution of this article lies in a newly generalized taxonomy for GSSL under the unified framework, with the most up-to-date references and valuable resources such as codes, datasets, and applications. Furthermore, we present several potential research directions as future work with our insights into this rapidly growing field.

Research on habitat quality assessment and decision-making based on Semi-supervised Ensemble Learning method—Daxia River Basin, China

Habitat quality is an indicator of the ecological evolution of a region, and evaluating habitat quality utilizing Machine Learning approaches can reflect the ecological status of a region more objectively. Gridded statistical ecological factors in the study region were utilized to build the Ecological Information State Layer (EISL) in the grid space. Calculate the Environmental Quality Index (EQI) of the sampled grid to evaluate the performance index of the models on the sampled grid. The model with the optimal performance index is selected for the task of habitat quality classification in the research region, and then especially decision-making advice is offered by inverting the degree of influence of ecological elements on habitat quality. The results show that: (1) The Semi-supervised Ensemble Learning model (Tri-training) Accuracy, Kappa coefficient, and F1-score are 0.93, 0.89, and 0.92, respectively, as the optimal model for the habitat quality classification task. (2) Based on the results of the ecological categories calculation, the current ecosystem quality of the southwestern section of the Daxia River Basin is maintained as a positive indicator. the ecosystem quality inside the living space centered on the cities of Linxia and Hezuo shows negative indicator changes. (3) The calculation results of the inverse importance of ecological factors show that vegetation index and human population density are the key factors impacting the habitat quality of the Daxia River Basin. In the ecological management of Daxia River Basin, the degree of spatial aggregation of people's settlements should be reduced while preserving and expanding vegetation cover. Using Tri-training's comprehensive analysis of multiple ecological factors, regional habitat quality can be accurately assessed.

Discriminative semisupervised dictionary learning method with graph embedding and pairwise constraints for crop disease image recognition

Crop diseases are serious biological disasters that affect agricultural production and ecological environment safety. Accurate detection and identification of disease types can effectively reduce the harm caused by diseases. However, there are various types of crop diseases and labeling them consumes a lot of labor and material resources. A discriminative semisupervised dictionary learning method with graph embedding and pairwise constraints (DSDL-GEPC) is proposed in this study. DSDL-GEPC adopts semisupervised dictionary learning as the baseline model, which combines the reconstruction error of all training samples and the classification error of labeled training samples. DSDL-GEPC consists of three learning terms. The graph embedding term is constructed based on dictionary atoms to obtain global geometric structural information of data samples. Using the pairwise constraint information of similar/heterogeneous images, the pairwise constraints term is constructed to enhance the dictionary's discriminability in terms of within-class similarity and between-class separability. In addition, the relaxed low-rank classification term is constructed, which introduces the ε-draggings technique and low-rank constraint on the relaxed label matrix to learn a discriminative classifier. Experiments are performed to compare the classification performance of DSDL-GEPC on the PlantVillage dataset. The results validate the effectiveness of DSDL-GEPC for crop disease image recognition.

On complementing unsupervised learning with uncertainty quantification

Pseudo-label-based semi-supervised learning (SSL) recently has achieved significant success in unlabeled data utilization. Recent success on pseudo-label-based SSL methods crucially hinges on thresholded pseudo-labeling and consistency regularization for the unlabeled data. However, most of the existing methods do not measure and incorporate the uncertainties due to the noisy pseudo-labels or out-of-distribution unlabeled samples. Therefore, the model’s discernment becomes noisier in real-life applications that involve a substantial amount of out-of-distribution unannotated data. This leads to slow convergence in the training process and poor generalization performance. Inspired by the recent developments in SSL, our goal in this paper is to propose a novel unsupervised uncertainty-aware objective and threshold-mediated pseudo-labeling scheme that rely on uncertainty quantification from aleatoric and epistemic sources. By incorporating recent techniques in SSL, our proposed uncertainty-aware framework can mitigate the issue of confirmation bias and the impact of noisy pseudo-labels, resulting in improved training efficiency and enhanced generalization performance within the SSL domain. Despite its simplicity and computational efficiency, our approach demonstrates improved performance compared to state-of-the-art SSL methods on challenging datasets, such as CIFAR-100 and the real-world dataset Semi-iNat.

Fault Detection and Diagnosis in Tennessee Eastman Process with Deep Autoencoder

Data-driven modeling has been considered as an attractive approach for fault detection in chemical processes. Of special interest to industry are methods that represent nonlinear phenomena and detect complex faults. In this paper, a semi-supervised deep learning method - deep autoencoder for fault detection in Tennessee Eastman Process (TEP) is proposed. The TEP process is a simulated benchmark for evaluating process control and monitoring methods. The performance of the proposed method is evaluated and compared to Principal Component Analysis (PCA). The experimental results demonstrate that the proposed optimized five-layers DAE model for fault detection outperforms the standard PCA. Of special importance to real-world applications is its capability for automatic variable selection. In comparison to PCA it demonstrated higher prediction accuracy for most of the generated faults. Deep autoencoder has the potential to become an excellent approach for process monitoring and fault detection in chemical processes.

HPFG: semi-supervised medical image segmentation framework based on hybrid pseudo-label and feature-guiding.

Semi-supervised learning methods have been attracting much attention in medical image segmentation due to the lack of high-quality annotation. To cope with the noise problem of pseudo-label in semi-supervised medical image segmentation and the limitations of contrastive learning applications, we propose a semi-supervised medical image segmentation framework, HPFG, based on hybrid pseudo-label and feature-guiding, which consists of a hybrid pseudo-label strategy and two different feature-guiding modules. The hybrid pseudo-label strategy uses the CutMix operation and an auxiliary network to enable the labeled images to guide the unlabeled images to generate high-quality pseudo-label and reduce the impact of pseudo-label noise. In addition, a feature-guiding encoder module based on feature-level contrastive learning is designed to guide the encoder to mine useful local and global image features, thus effectively enhancing the feature extraction capability of the model. At the same time, a feature-guiding decoder module based on adaptive class-level contrastive learning is designed to guide the decoder in better extracting class information, achieving intra-class affinity and inter-class separation, and effectively alleviating the class imbalance problem in medical datasets. Extensive experimental results show that the segmentation performance of the HPFG framework proposed in this paper outperforms existing semi-supervised medical image segmentation methods on three public datasets: ACDC, LIDC, and ISIC. Code is available at https://github.com/fakerlove1/HPFG .

An overview of research results and applications in the field of semi-supervised semantic segmentation

Traditional segmentation approaches, supervised deep learning methods, and semi-supervised deep learning methods have all found widespread use as the field of semi-supervised semantic segmentation has advanced. These methods have developed and progressed over time, opening up novel avenues of research in the field of image segmentation and giving potent resources for tackling difficult practical issues. These developments have deepened our understanding of image segmentation and provided flexible and efficient solutions to challenges in practical applications, ranging from classical traditional approaches to supervised methods based on deep learning, and beyond to semi-supervised methods that leverage both labeled and unlabeled data. Focusing on their specialized applications in medical and remote sensing image processing, this paper presents a complete overview of the development status of these methods. This study's image segmentation solutions can help tackle actual-world issues where annotated data is rare or expensive to some extent.

Semi-supervised contrastive regression for pharmaceutical processes

Artificial intelligence methods of time series are starting to play an increasing role in the pharmaceutical field, and in recent years, there have been significant advances in self-supervised representation learning for time series data. However, there are relatively few semi-supervised learning methods for time series, and there is almost no research on semi-supervised representation learning applicable to time series regression tasks. To address this gap, we propose a novel semi-supervised contrastive regression framework (SCRF), which combines two classical frameworks of representation learning. This framework is well-suited for regression problems of time series data from pharmaceutical processes and has been validated on a dataset collected during erythromycin production processes. Our experiments show that SCRF gets better performances than self-supervised and supervised methods, and it is more robust to missing labels, missing data, and random noise. The effectiveness of our novel contrastive learning framework and segmented augmentation methods is demonstrated through experiments.

Prototype-oriented contrastive learning for semi-supervised medical image segmentation

Semi-supervised learning has been recently explored to advance medical image segmentation due to the challenges of acquiring sufficient labeled data. However, the mainstream semi-supervised learning methods leverage unlabeled data by enforcing the perturbation-based prediction consistency, which neglects the semantic relations across different images and cannot handle inductive bias problem about the feature distribution. To address the above problems, in this paper, a novel prototype-oriented contrastive learning framework is proposed for semi-supervised medical image segmentation. Specifically, inspired from prototypical learning, a set of prototypes is extracted first to represent the diverse feature distributions of different images. Then we present semi-supervised contrastive learning with a prototype-oriented sampling strategy, encouraging the network to explore voxel-wise semantic relations across different images and learn more discriminative features to enhance the segmentation ability. The effectiveness of the proposed method is illustrated on three public benchmark datasets. Extensive experiments show that the proposed method outperforms several state-of-the-art semi-supervised segmentation methods, demonstrating its effectiveness for the challenging semi-supervised medical image segmentation task.

Consistency regularisation in varying contexts and feature perturbations for semi-supervised semantic segmentation of histology images

Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years, Deep Learning (DL) methods have been shown to perform well on segmentation tasks but DL methods generally require a large amount of pixel-wise annotated data. Pixel-wise annotation sometimes requires expert’s knowledge and time which is laborious and costly to obtain. In this paper, we present a consistency based semi-supervised learning (SSL) approach that can help mitigate this challenge by exploiting a large amount of unlabelled data for model training thus alleviating the need for a large annotated dataset. However, SSL models might also be susceptible to changing context and features perturbations exhibiting poor generalisation due to the limited training data. We propose an SSL method that learns robust features from both labelled and unlabelled images by enforcing consistency against varying contexts and feature perturbations. The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features. We show that cross-consistency training makes the encoder features invariant to different perturbations and improves the prediction confidence. Finally, entropy minimisation is employed to further boost the confidence of the final prediction maps from unlabelled data. We conduct an extensive set of experiments on two publicly available large datasets (BCSS and MoNuSeg) and show superior performance compared to the state-of-the-art methods.

Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness.

Semi-supervised learning method incorporating structural optimization for shear-wall structure design using small and long-tailed datasets

Intelligent structural design based on machine learning represents a novel structural design paradigm and has received extensive attention in recent years. However, the performance of the machine learning models is heavily dependent on the quality and quantity of training data, as the underlying approaches are inherently data-driven. Well-recognized data issues ‒ particularly data insufficiencies and long-tailed data distributions ‒ have become critical impediments in this research area. To address these data issues, this study formulates a schematic structural design task as a semi-supervised learning problem. Specifically, a semi-supervised learning method using small, long-tailed datasets is proposed in which a structural optimization method is incorporated into a self-training framework. As a practical application of the proposed method, a shear-wall layout optimization procedure is devised, based on a two-stage evaluation strategy and the previously established empirical design rules. Results of the numerical experiments indicate that the proposed method can improve the design performance on the tail data by 11.6 % and reduce the performance difference between the head and tail data by 21.3 %, compared to the conventional supervised learning method. A typical case study shows that the shear-wall layout created by the proposed method can satisfactorily resemble that by design engineers whilst meeting key code-specified requirements.