Multi-label Classification Model Research Articles

Convolution Neural Network Based Multi-Label Disease Detection Using Smartphone Captured Tongue Images

Purpose: Tongue image analysis for disease diagnosis is an ancient, traditional, non-invasive diagnostic technique widely used by traditional medicine practitioners. Deep learning-based multi-label disease detection models have tremendous potential for clinical decision support systems because they facilitate preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline where observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. Subjects, who consult collaborating physicians, voluntarily provide all images. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolutional neural network (MobileNetV2) model for binary classification. Once it predicts the diseased label, the disease prediction algorithm based on DenseNet-121 uses the image to diagnose single or multiple disease labels. Results: The MobileNetV2 architecture-based disease detection model achieved an average accuracy of 93% in distinguishing between diseased and normal, healthy tongues, whereas the multilabel disease classification model produced more than 90% accurate results for the disease class labels considered, strongly indicating a successful outcome with the smartphone-captured image dataset. Conclusion: AI-based image analysis shows promising results, and an extensive dataset could provide further improvements to this approach. Experimenting with smartphone images opens a great opportunity to provide preliminary health status to individuals at remote locations as well, prior to further treatment and diagnosis, using the concept of telemedicine.

Use of Natural Language Processing to Infer Sites of Metastatic Disease From Radiology Reports at Scale.

To evaluate natural language processing (NLP) methods to infer metastatic sites from radiology reports. A set of 4,522 computed tomography (CT) reports of 550 patients with 14 types of cancer was used to fine-tune four clinical large language models (LLMs) for multilabel classification of metastatic sites. We also developed an NLP information extraction (IE) system (on the basis of named entity recognition, assertion status detection, and relation extraction) for comparison. Model performances were measured by F1 scores on test and three external validation sets. The best model was used to facilitate analysis of metastatic frequencies in a cohort study of 6,555 patients with 53,838 CT reports. The RadBERT, BioBERT, GatorTron-base, and GatorTron-medium LLMs achieved F1 scores of 0.84, 0.87, 0.89, and 0.91, respectively, on the test set. The IE system performed best, achieving an F1 score of 0.93. F1 scores of the IE system by individual cancer type ranged from 0.89 to 0.96. The IE system attained F1 scores of 0.89, 0.83, and 0.81, respectively, on external validation sets including additional cancer types, positron emission tomography-CT ,and magnetic resonance imaging scans, respectively. In our cohort study, we found that for colorectal cancer, liver-only metastases were higher in de novo stage IV versus recurrent patients (29.7% v 12.2%; P < .001). Conversely, lung-only metastases were more frequent in recurrent versus de novo stage IV patients (17.2% v 7.3%; P < .001). We developed an IE system that accurately infers metastatic sites in multiple primary cancers from radiology reports. It has explainable methods and performs better than some clinical LLMs. The inferred metastatic phenotypes could enhance cancer research databases and clinical trial matching, and identify potential patients for oligometastatic interventions.

Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults.

Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality. The derivation set (n=516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n=380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n=10141). The mean ages of the derivation and external sets were 62.8 and 59.7years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P<0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5years, women 79%; median follow-up 4.9years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS+sarcopenia (aHR 1.65), osteoporosis+sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities. A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.

Label-text bi-attention capsule networks model for multi-label text classification

Multi-label text classification (MLTC) is the process of establishing relationships between documents and their corresponding labels. Previous research has focused on mining textual information, treating labels as information-less vectors in classification. This ignores the semantic and dependency relationships of labels. In real-life scenarios, the neglect of label information contradicts the classification process, which presents significant challenges for MLTC tasks. Label embedding partially resolves label information loss. Efficiently exploring semantic and dependency relationships of labels and their text connections remains a new challenge. In this paper, we propose a Label-Text Bi-Attention Capsule Networks (LTBACN) model for in-depth exploration of the dependency relationships between labels and text. Specifically, we first incorporate label information into nodes through label embedding, construct a graph structure to represent the dependency relationships between labels, and use Graph Convolutional Networks (GCN) to propagate information between nodes to further mine the relationships between labels. Subsequently, we employ a label-text bi-attention mechanism to learn the feature relationships between labels and text. The label-to-text attention mechanism extracts label-relevant text representations, while the text-to-label attention mechanism extracts the most relevant label representations for the text. We then merge these two types of feature representations to obtain fused representations that incorporate label-text bi-directional information. Finally, the fused features are fed into a capsule network classifier to capture multi-level semantic information and match the corresponding labels. The experimental results demonstrate that LTBACN outperforms other methods in terms of classification effectiveness. Compared to state-of-the-art methods, LTBACN achieves a significant improvement of 0.41%–0.68% in Micro−F1 measure, 0.52%–3.26% in Macro−F1 measure, 0.32%–2.18% in P@k measure, and 0.01%–1.18% in nDCG@k measure on the AAPD and RCV1-v2 datasets.

Compound fault diagnosis of diesel engines by combining generative adversarial networks and transfer learning

In order to solve the problem of compound fault diagnosis of diesel engine fuel injection system under the condition of few samples, a comprehensive diagnosis method based on generative adversarial networks and transfer learning based multi label classification models is proposed in this paper, which is based on convolutional attention networks. The generative adversarial networks are used to enhance the original data, and the enhanced data is used as the source data for transfer learning to ensure its effectiveness. Design multi-dimensional labels for compound fault diagnosis to enable the model to diagnose compound faults and their individual faults. Pre train the convolutional attention network with enhanced data generated from adversarial networks. Subsequently, the original data is used to integrate the feature information in the network and perform secondary training on the classification module. Finally, a compound fault diagnosis is implemented based on the model after secondary training. The effectiveness of the comprehensive diagnostic method proposed in the article is verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. The effectiveness of the comprehensive diagnostic method proposed in the article was verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. Compared to the method of directly training convolutional attention networks using raw data and the method of directly training convolutional attention networks without transfer learning using GAN enhanced datasets, the accuracy is improved by 16% and 8%, respectively.

Fit-For-Purpose Creativity Assessment: Automatic Scoring of the Test of Creative Thinking – Drawing Production (TCT-DP)

ABSTRACT Creativity is now universally recognized as an essential, 21st century competency. However, there are many practical barriers to the development of creativity in schools, universities and workplaces. Chief among these is a problem of the fitness-for-purpose of creativity assessments. Highly valid and reliable creativity tests, necessary to support the development of individual creativity, are typically slow and labor-intensive to administer and score. However, recent advances in machine learning are opening up solutions to the fitness-for-purpose problem, across a range of different modes (e.g. verbal and figural) of testing. This paper describes the development of an open-source, large, image-based, multi-label classification model capable of automatically scoring the figural Test of Creative Thinking – Drawing Production (TCT-DP) with accuracies comparable to those of typical human raters. The growth of this, and similar machine learning models, promises to transform the development of creativity in education, offering rapid, accurate and cost-effective assessments that can be seamlessly integrated into teaching and learning.

Evaluating the subjective perceptions of streetscapes using street-view images

Developing a model to evaluate urban streetscapes based on subjective perceptions is important for quantitative understanding. However, previous studies have only considered limited types of subjective perceptions, neglecting the relationships between them. Further, accurately measuring subjective perception with low computational costs for large-scale urban regions at high spatial resolutions has been difficult. We present a deep-learning-based multilabel classification model that can measure 22 subjective perceptions scores from street-view images. This model uses the results of a web questionnaire survey encompassing 22 subjective perceptions, with 8.8 million responses. Our model demonstrates high accuracy (0.80–0.91) in measuring subjective perception scores from street-view images and achieves low computational cost by training on 22 subjective perception relationships. The 22 subjective perceptions were analyzed using PCA and k-means analysis. By categorizing the 22 subjective perceptions into a two-dimensional space visualized and grouped into distinct groups—positive, negative, calm, and lively—we unearthed vital insights into the intricate nuances of human perception. In addition, the study used semantic segmentation to extract landscape elements from street-view images and applied ℓ1-regularized sparse modeling to identify the landscape elements structurally correlating with each subjective perception class. The analysis revealed that only seven out of nineteen landscape elements significantly correlated with subjective impressions, and these effects varied by class. Notably, sky coverage positively influences positive subjective perceptions, such as attractiveness and calmness, but negatively affects lively impressions. The proposed model can be used to map the overall image of a city and identify landscape design issues in community development design.

Training environmental sound classification models for real-world deployment in edge devices

The interest in smart city technologies has grown in recent years, and a major challenge is to develop methods that can extract useful information from data collected by sensors in the city. One possible scenario is the use of sound sensors to detect passing vehicles, sirens, and other sounds on the streets. However, classifying sounds in a street environment is a complex task due to various factors that can affect sound quality, such as weather, traffic volume, and microphone quality. This paper presents a deep learning model for multi-label sound classification that can be deployed in the real world on edge devices. We describe two key components, namely data collection and preparation, and the methodology to train the model including a pre-train using knowledge distillation. We benchmark our models on the ESC-50 dataset and show an accuracy of 85.4%, comparable to similar state-of-the-art models requiring significantly more computational resources. We also evaluated the model using data collected in the real world by early prototypes of luminaires integrating edge devices, with results showing that the approach works well for most vehicles but has significant limitations for the classes “person” and “bicycle”. Given the difference between the benchmarking and the real-world results, we claim that the quality and quantity of public and private data for this type of task is the main limitation. Finally, all results show great benefits in pretraining the model using knowledge distillation.

Compositional Generalization for Multi-Label Text Classification: A Data-Augmentation Approach

Despite significant advancements in multi-label text classification, the ability of existing models to generalize to novel and seldom-encountered complex concepts, which are compositions of elementary ones, remains underexplored. This research addresses this gap. By creating unique data splits across three benchmarks, we assess the compositional generalization ability of existing multi-label text classification models. Our results show that these models often fail to generalize to compositional concepts encountered infrequently during training, leading to inferior performance on tests with these new combinations. To address this, we introduce a data augmentation method that leverages two innovative text generation models designed to enhance the classification models' capacity for compositional generalization. Our experiments show that this data augmentation approach significantly improves the compositional generalization capabilities of classification models on our benchmarks, with both generation models surpassing other text generation baselines. Our codes available at https://github.com/yychai74/LD-VAE.

Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification

As a combination of emerging multi-view learning methods and traditional multi-label classification tasks, multi-view multi-label classification has shown broad application prospects. The diverse semantic information contained in heterogeneous data effectively enables the further development of multi-label classification. However, the widespread incompleteness problem on multi-view features and labels greatly hinders the practical application of multi-view multi-label classification. Therefore, in this paper, we propose an attention-induced missing instances imputation technique to enhance the generalization ability of the model. Different from existing incomplete multi-view completion methods, we attempt to approximate the latent features of missing instances in embedding space according to cross-view joint attention, instead of recovering missing views in kernel space or original feature space. Accordingly, multi-view completed features are dynamically weighted by the confidence derived from joint attention in the late fusion phase. In addition, we propose a multi-view multi-label classification framework based on label-semantic feature learning, utilizing the statistical weak label correlation matrix and graph attention network to guide the learning process of label-specific features. Finally, our model is compatible with missing multi-view and partial multi-label data simultaneously and extensive experiments on five datasets confirm the advancement and effectiveness of our embedding imputation method and multi-view multi-label classification model.

Limited-Supervised Multi-Label Learning with Dependency Noise

Limited-supervised multi-label learning (LML) leverages weak or noisy supervision for multi-label classification model training over data with label noise, which contain missing labels and/or redundant labels. Existing studies usually solve LML problems by assuming that label noise is independent of the input features and class labels, while ignoring the fact that noisy labels may depend on the input features (instance-dependent) and the classes (label-dependent) in many real-world applications. In this paper, we propose limited-supervised Multi-label Learning with Dependency Noise (MLDN) to simultaneously identify the instance-dependent and label-dependent label noise by factorizing the noise matrix as the outputs of a mapping from the feature and label representations. Meanwhile, we regularize the problem with the manifold constraint on noise matrix to preserve local relationships and uncover the manifold structure. Theoretically, we bound noise recover error for the resulting problem. We solve the problem by using a first-order scheme based on proximal operator, and the convergence rate of it is at least sub-linear. Extensive experiments conducted on various datasets demonstrate the superiority of our proposed method.

Predicting Chinese stock market using XGBoost multi-objective optimization with optimal weighting.

The application of artificial intelligence (AI) technology in various fields has been a recent research hotspot. As a representative technology of AI, the specific application of machine learning models in the field of economics and finance undoubtedly holds significant research value. This article proposes Extreme Gradient Boosting Multi-Objective Optimization Model with Optimal Weights (OW-XGBoost) to comprehensively balance the returns and risks of investment portfolios. The model utilizes fusing label with optimal weights to achieve multi-objective tasks, effectively controlling the impact of various risk and return indicators on the model, thus improving the interpretability and generalization ability of the model. In the experiments, we tested the model using China A-share data from October 2022 to April 2023 and conducted a series of robustness tests. The results indicate that: (1) The OW-XGBoost outperforms the XGBoost Model with Yield as Label (YL-XGBoost), XGBoost Multi-Label Classification Model (MLC-XGBoost) in controlling risk or achieving returns. (2) OW-XGBoost performs better overall compared to baseline models. (3) The robustness tests demonstrate that the model performs well under different market conditions, stock pools, and training set durations. The model performs best in moderately fluctuating stock markets, stock pools comprising high market value stocks, and training set durations measured in months. The methodology and results of this study provide a new perspective and approach for fundamental quantitative investment and also create new possibilities and avenues for the integration of AI, machine learning, and financial quantitative research.

Active Crowdsourcing for Multilabel Annotation.

Multilabel annotation is a critical step to generate training sets when learning classification models in various application domains, but asking domain experts to provide labels is usually time-consuming and expensive, which cannot meet the current requirement of the fast evolution of the models in the big data era. Although crowdsourcing provides a fast solution to acquire labels for multilabel learning, it faces the risk of high data acquisition cost and low label quality. This article proposes a novel one-coin label-dependent active crowdsourcing (OCLDAC) method to iteratively query noisy labels from crowd workers and learn multilabel classification models. In each iteration of active learning, integrated labels of instances are first inferred by a novel one-coin label-dependent model, which utilizes a mixture of multiple independent Bernoulli distributions to explore and exploit correlations among the labels to increase the accuracy of truth inference. Then, instances, labels, and workers are selected according to the novel strategies that incorporate the distribution of noisy labels, the prediction probability of learning models, label correlations, and the reliability of crowd workers. Simulations on eight multilabel datasets and evaluation on one real-world crowdsourcing dataset consistently show that the proposed OCLDAC significantly outperforms the state-of-the-art methods and their variants.

Correlation concept-cognitive learning model for multi-label classification

As a cognitive process, concept-cognitive learning (CCL) emphasizes the structured expression of data through systematic cognition and understanding, to obtain valuable information in the data. Although concept-cognitive learning has achieved good results in single-label classification tasks, it has not yet been applied to multi-label learning. The difficulty is that the existing concept-cognitive learning fails to effectively associate and utilize the structural relationships in the feature concepts and the multi-label concepts. In addition, in the face of complex multi-label datasets, it is difficult to obtain effective concepts for classification tasks. To solve these problems, this paper proposes a correlation concept-cognitive learning method and applies it to the multi-label classification (MLC) task. Moreover, the relationship between the feature concepts and the multi-label concepts is established by extent. On this basis, we comprehensively consider the extent relevancy and intent relevancy, and learn correlation concepts. In order to improve the classification precision, we construct correlation concept spaces to obtain the representation of effective concepts. Finally, we conducted experimental evaluations on ten datasets to illustrate the effectivity and advantages of the proposed approach.

Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText

Studying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categories. This study explores the enhancement of a multi-label classification model through the integration of FastText. Employing a CNN+Bi-LSTM architecture, the research undertakes the classification of Quranic translations across categories such as Tauhid, Ibadah, Akhlak, and Sejarah. Based on model evaluation using F1-Score, it shows significant differences between the CNN+Bi-LSTM model without FastText, with the highest result being 68.70% in the 80:20 testing configuration. Conversely, the CNN+Bi-LSTM+FastText model, combining embedding size and epoch parameters, achieves a result of 73.30% with an embedding size of 200, epoch of 100, and a 90:10 testing configuration. These findings underscore the significant impact of FastText on model optimization, with an enhancement margin of 4.6% over the base model.

Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning.

Instagram is a popular social networking platform for sharing photos with a large proportion of youth and young adult users. We aim to identify key features in anti-vaping Instagram image posts associated with high social media user engagement by artificial intelligence. We collected 8972 anti-vaping Instagram image posts and hand-coded 2200 Instagram images to identify nine image features such as warning signs and person-shown vaping. We utilized a deep-learning model, the OpenAI: contrastive language-image pre-training with ViT-B/32 as the backbone and a 5-fold cross-validation model evaluation, to extract similar features from the Instagram image and further trained logistic regression models for multilabel classification. Latent Dirichlet Allocation model and Valence Aware Dictionary and sEntiment Reasoner were used to extract the topics and sentiment from the captions. Negative binomial regression models were applied to identify features associated with the likes and comments count of posts. Several features identified in anti-vaping Instagram image posts were significantly associated with high social media user engagement (likes or comments), such as educational warnings and warning signs. Instagram posts with captions about health risks associated with vaping received significantly more likes or comments than those about help quitting smoking or vaping. Compared to the model based on 2200 hand-coded Instagram image posts, more significant features have been identified from 8972 AI-labeled Instagram image posts. Features identified from anti-vaping Instagram image posts will provide a potentially effective way to communicate with the public about the health effects of e-cigarette use. Considering the increasing popularity of social media and the current vaping epidemic, especially among youth and young adults, it becomes necessary to understand e-cigarette-related content on social media. Although pro-vaping messages dominate social media, anti-vaping messages are limited and often have low user engagement. Using advanced deep-learning and statistical models, we identified several features in anti-vaping Instagram image posts significantly associated with high user engagement. Our findings provide a potential approach to effectively communicate with the public about the health risks of vaping to protect public health.

Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus

Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%.

Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions

In the field of chemical synthesis planning, the accurate recommendation of reaction conditions is essential for achieving successful outcomes. This work introduces an innovative deep learning approach designed to address the complex task of predicting appropriate reagents, solvents, and reaction temperatures for chemical reactions. Our proposed methodology combines a multi-label classification model with a ranking model to offer tailored reaction condition recommendations based on relevance scores derived from anticipated product yields. To tackle the challenge of limited data for unfavorable reaction contexts, we employed the technique of hard negative sampling to generate reaction conditions that might be mistakenly classified as suitable, forcing the model to refine its decision boundaries, especially in challenging cases. Our developed model excels in proposing conditions where an exact match to the recorded solvents and reagents is found within the top-10 predictions 73% of the time. It also predicts temperatures within ± 20 ∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }{} {\ extbf {C}}$$\\end{document} of the recorded temperature in 89% of test cases. Notably, the model demonstrates its capacity to recommend multiple viable reaction conditions, with accuracy varying based on the availability of condition records associated with each reaction. What sets this model apart is its ability to suggest alternative reaction conditions beyond the constraints of the dataset. This underscores its potential to inspire innovative approaches in chemical research, presenting a compelling opportunity for advancing chemical synthesis planning and elevating the field of reaction engineering.Scientific contributionThe combination of multi-label classification and ranking models provides tailored recommendations for reaction conditions based on the reaction yields. A novel approach is presented to address the issue of data scarcity in negative reaction conditions through data augmentation.Graphical

Multiple marine algae identification based on three-dimensional fluorescence spectroscopy and multi-label convolutional neural network

Accurate identification of algal populations plays a pivotal role in monitoring seawater quality. Fluorescence-based techniques are effective tools for quickly identifying different algae. However, multiple coexisting algae and their similar photosynthetic pigments can constrain the efficacy of fluorescence methods. This study introduces a multi-label classification model that combines a specific Excitation-Emission matric convolutional neural network (EEM-CNN) with three-dimensional (3D) fluorescence spectroscopy to detect single and mixed algal samples. Spectral data can be input directly into the model without transforming into images. Rectangular convolutional kernels and double convolutional layers are applied to enhance the extraction of balanced and comprehensive spectral features for accurate classification. A dataset comprising 3D fluorescence spectra from eight distinct algae species representing six different algal classes was obtained, preprocessed, and augmented to create input data for the classification model. The classification model was trained and validated using 4448 sets of test samples and 60 sets of test samples, resulting in an accuracy of 0.883 and an F1 score of 0.925. This model exhibited the highest recognition accuracy in both single and mixed algae samples, outperforming comparative methods such as ML-kNN and N-PLS-DA. Furthermore, the classification results were extended to three different algae species and mixed samples of skeletonema costatum to assess the impact of spectral similarity on multi-label classification performance. The developed classification models demonstrated robust performance across samples with varying concentrations and growth stages, highlighting CNN's potential as a promising tool for the precise identification of marine algae.

Multi-Label Text Classification Model Based on Multi-Level Constraint Augmentation and Label Association Attention

In the multi-label text classification task, a text usually corresponds to multiple label categories, and the labels have correlation and hierarchical structure. However, when the label hierarchy is unknown, the number of various labels is not balanced, which makes it difficult for the model to classify low-frequency labels. In addition, labels have semantic similarities that make it difficult for the model to distinguish between them. In this article, we propose a multi-label text classification model based on multi-level constraint augmentation and label association attention. Compared with traditional methods, our method has two contributions: (1) In order to alleviate the problem of unbalanced number of different label categories and ensure the rationality of sample generation, we propose a data augmentation method based on multi-level constraints. In the process of sample generation, this method uses historical generation information, sample original text information, and sample topic to constrain the generated text. (2) In order to make the model recognize the associated labels accurately, we propose an interaction mechanism based on label association attention and filter gate. This method combines text information and label weight information. At the same time, our classification model considers the important weights of text sentences and effectively utilizes the co-occurrence relationship between labels. Experimental results on three benchmark datasets show that our model outperforms state-of-the-art methods on all main evaluation metrics, especially on low-frequency label prediction with sparse samples.