Accuracy Of Multi-label Classification Research Articles

A novel random fast multi-label deep forest classification algorithm

With the development of big data, a large amount of multi-label data has emerged in various fields. The use of deep classification models to solve multi-label classification problems is the current research hotspot. However, the existing deep classification algorithms still face two great challenges in solving the multi-label classification problem: the poor multi-label classification accuracy and the high time cost. In order to solve the above challenges, this paper proposes a novel random fast multi-label deep forest classification algorithm (RFDF). Firstly, the label-by-label approach is adopted for the training and prediction of base classifiers, while the multi-label cascade model is constructed, so as to obtain better multi-label classification results. Then, an adaptive method is proposed to determine the number of base classifiers, and a random features partitioning strategy is provided to generate the initial base classifier, achieving parallel training of the base classifier. Finally, the proposed RFDF is compared with the existing advanced algorithms, and the experimental results show that the RFDF achieves better results on most of the metrics of the ten datasets, and obtains lower time cost compared to VDSDF and better stability.

Domain knowledge-powered attention for air traffic management hazardous events classification

The automated classification of hazardous events in Air Traffic Management (ATM) involves concise texts with specialized terminology. The problem is currently addressed by machine learning text classification methods; however, some methods overlook domain knowledge, while others require it excessively. This research aims to identify a process for integrating low-cost, text-based domain knowledge into classification to address the issue of domain shift. To achieve this, the Wide and Deep Bidirectional Encoder Representations from Transformers (WD-BERT) model is proposed, featuring a unique knowledge-powered attention mechanism. WD-BERT's Wide Attention module assesses the compatibility between input text and domain knowledge to determine class probabilities, while its Deep Attention module extracts contextual features guided by domain knowledge. Additionally, a text mining method is employed to extract domain knowledge from ATM regulation texts. The model is trained and evaluated using a dataset derived from the China ATM Hazard Source Database. It achieves a multi-label classification accuracy of 80.24% and an F1-micro score of 0.88, outperforming comparative models with state-of-the-art performance. Breakdown analyses of the model indicate that the carefully designed attention mechanism endows WD-BERT with the ability to integrate domain knowledge, allowing it to excel in contexts involving complex sentences, multiple terms, and domain shifts. The innovation of this study lies in the proposed domain knowledge-powered attention mechanism, which allows simply organized domain knowledge to effectively guide ATM hazardous events classification, alleviating the impact of domain shift. This method can also be applied to other vertical domains without knowledge graphs.

A Multitask Dynamic Graph Attention Autoencoder for Imbalanced Multilabel Time Series Classification.

Graph learning is widely applied to process various complex data structures (e.g., time series) in different domains. Due to multidimensional observations and the requirement for accurate data representation, time series are usually represented in the form of multilabels. Accurately classifying multilabel time series can provide support for personalized predictions and risk assessments. It requires effectively capturing complex label relevance and overcoming imbalanced label distributions of multilabel time series. However, the existing methods are unable to model label relevance for multilabel time series or fail to fully exploit it. In addition, the existing multilabel classification balancing strategies suffer from limitations, such as disregarding label relevance, information loss, and sampling bias. This article proposes a dynamic graph attention autoencoder-based multitask (DGAAE-MT) learning framework for multilabel time series classification. It can fully and accurately model label relevance for each instance by using a dynamic graph attention-based graph autoencoder to improve multilabel classification accuracy. DGAAE-MT employs a dual-sampling strategy and cooperative training approach to improve the classification accuracy of low-frequency classes while maintaining the classification accuracy of high-frequency and mid-frequency classes. It avoids information loss and sampling bias. DGAAE-MT achieves a mean average precision (mAP) of 0.955 and an F1 score of 0.978 on a mixed medical time series dataset. It outperforms state-of-the-art works in the past two years.

Image to Label to Answer: An Efficient Framework for Enhanced Clinical Applications in Medical Visual Question Answering

Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering clinical application and advancement. This paper proposes the ITLTA framework for Med-VQA, designed based on field requirements. ITLTA combines multi-label learning of medical images with the language understanding and reasoning capabilities of large language models (LLMs) to achieve zero-shot learning, meeting natural language module needs without end-to-end training. This approach reduces deployment costs and training data requirements, allowing LLMs to function as flexible, plug-and-play modules. To enhance multi-label classification accuracy, the framework uses external medical image data for pretraining, integrated with a joint feature and label attention mechanism. This configuration ensures robust performance and applicability, even with limited data. Additionally, the framework clarifies the decision-making process for visual labels and question prompts, enhancing the interpretability of Med-VQA. Validated on the VQA-Med 2019 dataset, our method demonstrates superior effectiveness compared to existing methods, confirming its outstanding performance for enhanced clinical applications.

GCN-LSTM: multi-label educational emotion prediction based on graph Convolutional network and long and short term memory network fusion label correlation in online social networks

Although there are a lot of methods for multi-label classification in the past research, there are still many problems. For example, in the real world, labels are not necessarily independent of each other, and there may be some connection between labels. Therefore, exploring and utilizing the interdependence between labels is a key issue in current research. For example, in the photo category, a picture that contains blue sky often also contains white clouds, and in the text category, a political story is less likely to be entertainment news. Therefore, the key to improve the accuracy of multi-label classification is to effectively learn the possible correlations between each label. Therefore, we propose a novel multi-label educational emotion prediction based on graph convolutional network and long and short term memory network fusion label correlation in online social networks. This model uses Word2Vec method to train word vectors and combines graph convolutional neural network (GCN) with long and short term memory network (LSTM). The GCN is used to dig deeper word features of text, the LSTM layer is used to learn the longterm dependence relationship between words, and the multi-granularity attention mechanism is used to assign higher weight to the affective word features. At the same time, label correlation matrix is used to complete the label feature vector and text features as the input of the classifier, and the correlation between labels is investigated. The experimental results on the open data set show that the proposed model has a good classification effect compared with other advanced methods. The research results promote the combination of deep learning and affective computing, and can promote the research of network user behavior analysis and prediction, which can be used in personalized recommendation, targeted advertising and other fields, and has wide academic significance and application prospects.

Intelligent detection on construction project contract missing clauses based on deep learning and NLP

PurposeThe number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.Design/methodology/approachA complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.Findings1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.Originality/valueNLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning.

In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable's impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model's multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively.

Deciphering simultaneous heart conditions with spectrogram and explainable-AI approach

The sounds made by the heart are regularly used to determine the mechanics and status of blood flow within the heart. While machine-learning algorithms have been presented to classify different heart conditions via auscultation, these models typically output a single-label decision and offer little insight into the magnitude or severity of the classified condition. The inner workings of these networks are a ‘black box’, offering a diagnosis prediction without a metric for explaining how the conclusion was drawn. Here, we present a novel approach, combining deep convolutional neural networks and explainable AI algorithms, to extract key temporal signatures in recordings of multiple heart conditions to enable multi-label classification and severity determination. Signatures found are supported by previous research and offer more information than a simple single-label classification algorithm. The neural networks are able to reach a multi-label classification accuracy of up to 78% (alongside 98% single-label accuracy) after being trained solely on single-label data. This approach may assist physicians in making quick, accurate, and comprehensive diagnoses and provide new insights for the progress of multi-label machine learning for medical diagnosis.

Multi-Label Feature Selection with Conditional Mutual Information.

Feature selection is an important way to optimize the efficiency and accuracy of classifiers. However, traditional feature selection methods cannot work with many kinds of data in the real world, such as multi-label data. To overcome this challenge, multi-label feature selection is developed. Multi-label feature selection plays an irreplaceable role in pattern recognition and data mining. This process can improve the efficiency and accuracy of multi-label classification. However, traditional multi-label feature selection based on mutual information does not fully consider the effect of redundancy among labels. The deficiency may lead to repeated computing of mutual information and leave room to enhance the accuracy of multi-label feature selection. To deal with this challenge, this paper proposed a multi-label feature selection based on conditional mutual information among labels (CRMIL). Firstly, we analyze how to reduce the redundancy among features based on existing papers. Secondly, we propose a new approach to diminish the redundancy among labels. This method takes label sets as conditions to calculate the relevance between features and labels. This approach can weaken the impact of the redundancy among labels on feature selection results. Finally, we analyze this algorithm and balance the effects of relevance and redundancy on the evaluation function. For testing CRMIL, we compare it with the other eight multi-label feature selection algorithms on ten datasets and use four evaluation criteria to examine the results. Experimental results illustrate that CRMIL performs better than other existing algorithms.

A Systematic Literature Review on Multi-Label Classification based on Machine Learning Algorithms

Multi-label classification is a technique used for mapping data from single labels to multiple labels. These multiple labels stand part of the same label set comprising inconsistent labels. The objective of multi-label classification is to create a classification model for previously unidentified samples. The accuracy of multi-label classification based on machine learning algorithms has been a particular study and discussion topic for researchers. This research aims to present a systematic literature review on multi-label classification based on machine learning algorithms. This study also discusses machine learning algorithm techniques and methods for multi-label classification. The findings would help researchers to explore and find the best accuracy of multi-label classification. The review result considered the Support Vector Machine (SVM) as the most accurate and appropriate machine learning algorithm in multi-label classification.

Complexity Graph-Based Multilabel Classification Method of Human Action in Rope Skipping Scene

Aiming at the problem of insufficient accuracy of multilabel classification of human action at present, a multilabel classification method of human action in the rope skipping scene is proposed. It realizes feature recognition and classification by collecting human action features in the scene of skipping rope movement and uses RNN to optimize the human action feature recognition algorithm. On the basis of feature recognition, the characteristics of human movement in the rope skipping scene are classified, the confidence map of the key point position is obtained by using the Gaussian modeling method, and the action multilabel classification is realized. Finally, experiments show that the multilabel classification method of human action in rope skipping scene has high accuracy and fully meets the research requirements.

Improved Double‐Layer Structure Multilabel Classification Model via Optimal Sequence and Attention Mechanism

Multilabel classification is a key research topic in the machine learning field. In this study, the author put forward a two/two‐layer chain classification algorithm with optimal sequence based on the attention mechanism. This algorithm is a classification model with a two‐layer structure. By introducing an attention mechanism, this study analyzes the key attributes to achieve the goal of classification. To solve the problem of algorithm accuracy degradation caused by the order of classifiers, we adopt the OSS (optimal sequence selection) algorithm to find the optimal sequence of tags. The test results based on the actual dataset show that the ATDCC‐OS algorithm has good performance on all performance evaluation metrics. The average accuracy of this algorithm is over 80%. The microaverage AUC performance reaches 0.96. In terms of coverage performance, its coverage performance is below 10%. The comprehensive result of single error performance is the best. The loss performance is about 0.03. The purpose of the ATDCC‐OS algorithm proposed in the study is to help improve the accuracy of multilabel classification so as to obtain more effective data information.

A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study

MotivationMachine learning (ML) algorithms can provide better cardiovascular event (CVE) prediction. However, ML algorithms are mostly explored for predicting a single CVE at a time. The objective of this study is to design and develop an ML-based system to predict multi-label CVEs, such as (i) coronary artery disease, (ii) acute coronary syndrome, and (iii) a composite CVE—a class of AtheroEdge 3.0 (ML) system. MethodsFocused carotid B-mode ultrasound and coronary angiography are performed on a group of 459 participants consisting of three cardiovascular labels. Initially, 23 risk predictors comprising (i) patients’ demographics, (ii) clinical blood-biomarkers, and (iii) carotid ultrasound image-based phenotypes are collected. Six types of classification techniques comprising (a) four problem transformation methods (PTM) and (b) two algorithm adaptation methods (AAM) are used for multi-label CVE prediction. The performance of the proposed system is evaluated for accuracy, sensitivity, specificity, F1-score, and area-under-the-curve (AUC) using 10-fold cross-validation. The proposed system is also verified using another database of 522 participants. ResultsFor the primary database, PTM demonstrated a better multi-label CVE prediction than AAM (mean accuracy: 80.89% vs. 62.83%, mean AUC: 0.89 vs. 0.63), validating our hypothesis. The PTM-based binary relevance (BR) technique provided optimal performance in multi-label CVE prediction. The overall multi-label classification accuracy, sensitivity, specificity, F1-score, and AUC using BR are 81.2 ± 3.01%, 76.5 ± 8.8%, 83.8 ± 3.8%, 75.37 ± 5.8%, and 0.89 ± 0.02 (p < 0.0001), respectively. When used on the second Canadian database with seven cardiovascular events (acute coronary syndrome, myocardial infarction, angina, stroke, transient ischemic attack, heart failure, and death), the proposed system showed an accuracy of 96.36 ± 0.87% (AUC: 0.61 ± 0.06, p < 0.0001). ConclusionML-based multi-label classification algorithms, such as binary relevance, yielded the best predictions for three cardiovascular endpoints.

FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification.

In production, due to natural conditions or process peculiarities, a single product often may exhibit more than one type of defect. The accurate identification of all defects has an important guiding significance and practical value to improve the planting and production processes. Concerning the surface defect classification task, convolutional neural networks can be implemented as a powerful instrument. However, a typical convolutional neural network tends to consider an image as an inseparable entity and a single instance when extracting features; moreover, it may overlook semantic correlations between different labels. To address these limitations, in the present paper, we proposed a feature-wise attention-based relation network (FAR-Net) for multilabel jujube defect classification. The network included four different modules designed for (1) image feature extraction, (2) label-wise feature aggregation, (3) feature activation and deactivation, and (4) correlation learning among labels. To evaluate the proposed method, a unique multilabel jujube defect dataset was constructed as a benchmark for the multilabel classification task of the jujube defect images. The results of experiments show that owing to the relation learning mechanism, the average precision of the three main composite defects in the dataset increases by 5.77%, 4.07%, and 3.50%, respectively, compared to the backbone of our network, namely Inception v3, which indicated that the proposed FAR-Net effectively facilitated the learning of correlation between labels and eventually, improved the multilabel classification accuracy.

Spatial division networks for weakly supervised detection

With only global image-level annotations, weakly supervised learning of deep convolutional neural networks has shown enough capacity in classification and localization but lack of ability to present the detection explicitly. In this work, we propose a novel spatial division network, which is applied to detect bounding boxes only with weak supervision. The essence of our model is two innovative differentiable modules, determination network and parameterized division, which perform the spatial division in feature maps of classification networks. After training, the learned parameters of the spatial division would correspond to a set of predicted bounding box coordinates. To demonstrate the effectiveness of our model for multi-label classification and weakly supervised detection, we conduct extensive experiments on the multi-MNIST dataset. Experimental results show our spatial division networks can (1) help improve the accuracy of multi-label classification, (2) implement in an end-to-end way only with the image-level annotations, and (3) output accurate bounding box coordinate, thereby achieving multi-digits detection.

Multi-Population Genetic Algorithm for Multilabel Feature Selection Based on Label Complementary Communication.

Multilabel feature selection is an effective preprocessing step for improving multilabel classification accuracy, because it highlights discriminative features for multiple labels. Recently, multi-population genetic algorithms have gained significant attention with regard to feature selection studies. This is owing to their enhanced search capability when compared to that of traditional genetic algorithms that are based on communication among multiple populations. However, conventional methods employ a simple communication process without adapting it to the multilabel feature selection problem, which results in poor-quality final solutions. In this paper, we propose a new multi-population genetic algorithm, based on a novel communication process, which is specialized for the multilabel feature selection problem. Our experimental results on 17 multilabel datasets demonstrate that the proposed method is superior to other multi-population-based feature selection methods.

Multilabel naïve Bayes classification considering label dependence

Multilabel classification is the task of assigning relevant labels to an instance, and it has received considerable attention in recent years. This task can be performed by extending a single-label classifier, such as the naïve Bayes classifier, to utilize the useful relations among labels for achieving better multilabel classification accuracy. However, the conventional multilabel naïve Bayes classifier treats each label independently and hence neglects the relations among labels, resulting in degenerated accuracy. We propose a new multilabel naïve Bayes classifier that considers the relations or dependence among labels. Experimental results show that the proposed method outperforms conventional multilabel classifiers.

A Deep Multi-Attention Driven Approach for Multi-Label Remote Sensing Image Classification

Deep learning (DL) based methods have been found popular in the framework of remote sensing (RS) image scene classification. Most of the existing DL based methods assume that training images are annotated by single-labels, however RS images typically contain multiple classes and thus can simultaneously be associated with multi-labels. Despite the success of existing methods in describing the information content of very high resolution aerial images with RGB bands, any direct adaptation for high-dimensional high-spatial resolution RS images falls short of accurate modeling the spectral and spatial information content. To address this problem, this paper presents a novel approach in the framework of the multi-label classification of high dimensional RS images. The proposed approach is based on three main steps. The first step describes the complex spatial and spectral content of image local areas by a novel KBranch CNN that includes spatial resolution specific CNN branches. The second step initially characterizes the importance scores of different local areas of each image and then defines a global descriptor for each image based on these scores. This is achieved by a novel multi-attention strategy that utilizes the bidirectional long short-term memory networks. The final step achieves the classification of RS image scenes with multilabels. Experiments carried out on BigEarthNet (which is a large-scale Sentinel-2 benchmark archive) show the effectiveness of the proposed approach in terms of multi-label classification accuracy compared to the state-of-the-art approaches. The code of the proposed approach is publicly available at https://gitlab.tubit.tuberlin.de/rsim/MAML-RSIC.

Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multilabel classification with a budget constraint. The proposed method employs a novel exploration operation to enhance the search capabilities of a traditional genetic search, resulting in improved multilabel classification. Empirical studies using 20 real-world datasets demonstrate that the proposed method outperforms conventional multilabel feature selection methods.

A novel attribute reduction approach for multi-label data based on rough set theory

Multi-label classification is an active research field in machine learning. Because of the high dimensionality of multi-label data, attribute reduction (also known as feature selection) is often necessary to improve multi-label classification performance. Rough set theory has been widely used for attribute reduction with much success. However, little work has been done on applying rough set theory to attribute reduction in multi-label classification. In this paper, a novel attribute reduction method based on rough set theory is proposed for multi-label data. First, the uncertainties conveyed by labels are analyzed, and a new type of attribute reduct is introduced, called complementary decision reduct. The relationships between complementary decision reduct and two representative types of attribute reducts are also investigated, showing significant advantages of complementary decision reduct in revealing the uncertainties implied in multi-label data. Second, a discernibility matrix-based approach is introduced for computing all complementary decision reducts, and a heuristic algorithm is proposed for effectively computing a single complementary decision reduct. Experiments on real-life data demonstrate that the proposed approach can effectively reduce unnecessary attributes and improve multi-label classification accuracy.