ADVANCED TECHNIQUES IN MULTI-LABEL TEXT CLASSIFICATION: INTEGRATION OF BETA ANT COLONY AND DEEP LEARNING APPROACHES

Multi-label text classification has a wide range of applications in the real world. However, the data distribution in the real world is often imbalanced, which leads to serious long-tailed problems. For multi-label classification, due to the vast scale of datasets and existence of label co-occurrence, how to effectively improve the prediction accuracy of tail labels without degrading the overall precision becomes an important challenge. To address this issue, we propose A Dual-Branch Learning Model with Gradient-Balanced Loss (DBGB) based on the paradigm of existing pre-trained multi-label classification SOTA models. Our model consists of two main long-tailed module improvements. First, with the shared text representation, the dual-classifier is leveraged to process two kinds of label distributions; one is the original data distribution and the other is the under-sampling distribution for head labels to strengthen the prediction for tail labels. Second, the proposed gradient-balanced loss can adaptively suppress the negative gradient accumulation problem related to labels, especially tail labels. We perform extensive experiments on three multi-label text classification datasets. The results show that the proposed method achieves competitive performance on overall prediction results compared to the state-of-the-art methods in solving the multi-label classification, with significant improvement on tail-label accuracy.

Boosting ICD multi-label classification of health records with contextual embeddings and label-granularity

In multi-label text classification, the central aim is to associate an array of descriptive labels for a better understanding of the text. There are three main challenges in doing multi-label text classification (i) a large number of text (input) features, (ii) the underlying implicit relationship between input features and output labels, and (iii) an implicit inter-label dependency. In traditional approaches to multi-label classification, these problems are not being addressed collectively. A feature selection strategy that inherently uses local features to discriminate a class and similarly global features that can distinctly separate classes can be very effective for multi-label classification. In this research, we perform a feature selection and ranking strategy based on local and global features. A Naïve Bayes classifier is being used using a combination of these two -feature sets, it is compared with the baseline implemented with the term frequency-inverse document frequency (TF-IDF). A series of experiments have been carried out on standard multi-label text datasets, using evaluation metrics like Hamming loss, Subset Accuracy and Micro/Macro F1 scores, and encouraging results are obtained.

In multi-label text classification (MLTC), each given document is associated with a set of correlated labels. To capture label correlations, previous classifier-chain and sequence-to-sequence models transform MLTC to a sequence prediction task. However, they tend to suffer from label order dependency, label combination over-fitting and error propagation problems. To address these problems, we introduce a novel approach with multi-task learning to enhance label correlation feedback. We first utilize a joint embedding (JE) mechanism to obtain the text and label representation simultaneously. In MLTC task, a document-label cross attention (CA) mechanism is adopted to generate a more discriminative document representation. Furthermore, we propose two auxiliary label co-occurrence prediction tasks to enhance label correlation learning: 1) Pairwise Label Co-occurrence Prediction (PLCP), and 2) Conditional Label Co-occurrence Prediction (CLCP). Experimental results on AAPD and RCV1-V2 datasets show that our method outperforms competitive baselines by a large margin. We analyze low-frequency label performance, label dependency, label combination diversity and coverage speed to show the effectiveness of our proposed method on label correlation learning.

Multi-label classification (MLC) deals with the task of assigning an instance to all its relevant classes. This task becomes challenging in the presence of the label dependencies. The MLC methods that assume label independence do not use the dependencies among labels. We present a two-stage framework which improves the performance of MLC by using label dependencies. In the first stage, a standard MLC method is used to get the confidence scores for different labels. A conditional random field (CRF) is used in the second stage that improves the performance of the first-stage MLC by using the label dependencies among labels. An optimization-based framework is used to learn the structure and parameters of the CRF. Experiments show that the proposed model performs better than the state-of-the-art methods for MLC.

Text classification (TC) is an important basic task in the field of Natural Language Processing (NLP), and multi-label text classification (MLTC) is an important branch of TC. MLTC has undergone a transformation from traditional machine learning to deep learning, and various models with excellent performance have emerged one after another. But at present, the focus of various related researches is also varied, so we combed the excellent research results in the field of MLTC in recent years, and classified them according to the focus of their research. At the same time, we also summarized the relevant data sets and evaluation indicators in the field of multi-label text classification, and made a prospect for the future of the field of MLTC.KeywordsNatural Language ProcessingText classificationMulti-labelDeep learning

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. Most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics. However, how to coherently take them into account is still far from studied. In addition, most existing methods treat output labels as independent medoids, ignoring the hierarchical relationships among them, which leads to a substantial loss of useful semantic information. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding word matrix representation preserving both the non-consecutive, long-distance and local sequential semantics. Then the word matrix is input to the proposed attentional graph capsule recurrent CNNs for effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches.

Multi-label text classification is generally more difficult for its exponential output label space and more flexible input document considering its corresponding number of labels. We observed that some long documents have only one or two labels while some short documents are related to much more labels. In this paper, we propose a dynamic Gated Neural Network architecture to simultaneously process the document through two parts, one to extract the most informative semantics and filter redundant information, the other to capture context semantics with most of the information in the document kept. And semantics from these two parts are dynamically controlled by a gate to perform subsequent classification. And to better the training we incorporate label dependencies into traditional binary cross-entropy loss by exploiting label co-occurrences. Experimental results on AAPD and RCV1-V2 datasets show that our proposed methods achieve state-of-art performance. Further analysis of experimental results demonstrate that the proposed methods not only capture enough feature information from both long and short documents assigned with various labels, but also exploit label dependency to regularize the proposed model to further improve its performance.

Investigating the learning approaches of students in nursing education

Digital transformation refers to an organization’s use of digital technology to improve its products, services, and operations, aligning them with evolving business requirements. To demonstrate this transformative process, we present a real-life case study where a company seeks to automate the classification of their textual data rather than relying on manual methods. Transitioning to automated classification involves deploying machine learning models, which rely on pre-labeled datasets for training and making predictions on new data. However, upon receiving the dataset from the company, we faced challenges due to the imbalanced distribution of labels and moderately large label spaces. To tackle text classification with such a business dataset, we evaluated four distinct methods for multi-label text classification: fine-tuned Bidirectional Encoder Representations from Transformers (BERT), Binary Relevance, Classifier Chains, and Label Powerset. The results revealed that fine-tuned BERT significantly outperformed the other methods across key metrics like Accuracy, F1-score, Precision, and Recall. Binary Relevance also displayed competence in handling the dataset effectively, while Classifier Chains and Label Powerset exhibited comparatively less impressive performance. These findings highlight the remarkable effectiveness of fine-tuned BERT model and the Binary Relevance classifier in multi-label text classification tasks, particularly when dealing with imbalanced training datasets and moderately large label spaces. This positions them as valuable assets for businesses aiming to automate data classification in the digital transformation era.

Multi-label text classification is an increasingly important field as large amounts of text data are available and extracting relevant information is important in many application contexts. Probabilistic generative models are the basis of a number of popular text mining methods such as Naive Bayes or Latent Dirichlet Allocation. However, Bayesian models for multi-label text classification often are overly complicated to account for label dependencies and skewed label frequencies while at the same time preventing overfitting. To solve this problem we employ the same technique that contributed to the success of deep learning in recent years: greedy layer-wise training. Applying this technique in the supervised setting prevents overfitting and leads to better classification accuracy. The intuition behind this approach is to learn the labels first and subsequently add a more abstract layer to represent dependencies among the labels. This allows using a relatively simple hierarchical topic model which can easily be adapted to the online setting. We show that our method successfully models dependencies online for large-scale multi-label datasets with many labels and improves over the baseline method not modeling dependencies. The same strategy, layer-wise greedy training, also makes the batch variant competitive with existing more complex multi-label topic models.

Multi-label text classification (MLTC) refers to the problem of dealing with textual data classification based on multiple labels or tags. There are numerous real-world scenarios where the need for assigning labels to a particular object arises, such that the labels are descriptive of the properties of that object. However, in real life, it is not uncommon for one object to hold more than a singular property describing itself, hence it needs multiple labels to be associated with it. In the cases of textual data, one such scenario is assigning labels to a movie describing the genre, which needs more than one genre to specify the plot in a practical scenario. This makes movie genre prediction the desired choice for multi-label classification in many kinds of literature. This paper explores and presents an in-depth analysis of the approach of solving movie-genre prediction problems using the sequential model with universal sentence encoder (USE) for text encoding, alongside the use of label powerset (LP) as the problem transformation approach. Along with that, a comparative analysis of the performance of the model with different optimizers is performed. The best outcome achieved is f1-score 0.69 and accuracy of 0.89 with Adam optimizer, which, upon comparison with other literature of the similar domain, is either an equal or better in performance.

Multi-label text classification (MLTC) is a significant task that aims to assign multiple labels to each given text. There are usually correlations between the labels in the dataset. However, traditional machine learning methods tend to ignore the label correlations. To capture the dependencies between the labels, the sequence-to-sequence (Seq2Seq) model is applied to MLTC tasks. Moreover, to reduce the incorrect penalty caused by the Seq2Seq model due to the inconsistent order of the generated labels, a deep reinforced sequence-to-set (Seq2Set) model is proposed. However, the label generation of the Seq2Set model still relies on a sequence decoder, which cannot eliminate the influence of the predefined label order and exposure bias. Therefore, we propose an MLTC model with latent word-wise label information (MLC-LWL), which constructs effective word-wise labeled information using a labeled topic model and incorporates the label information carried by the word and label context information through a gated network. With the word-wise label information, our model captures the correlations between the labels via a label-to-label structure without being affected by the predefined label order or exposure bias. Extensive experimental results illustrate the effectiveness and significant advantages of our model compared with the state-of-the-art methods.

This article describes the code that was used in a multi-topic labeling system, the code starts with loading the train, validation, and test data set then installing the pyarabic and Simple Transformers libraries." pyarabic" allows the system to manipulate Arabic letters. "Simple Transformers" is a Natural Language Processing (NLP) library designed to simplify the usage of Transformer models without having to compromise on utility. The model was used from the Simple Transformer library is "Multi Label Classification Model" with the model type "bert" and the model's name "asafaya/bert-base-arabic". In multi-label text classification, the target for a single article (row) from the training dataset is a list of 10 distinct binary labels. A transformer-based multi-label text classification model typically consists of a transformer model with a classification layer on top of it. The results were impressive compared to the short training time for two epochs, which is five minutes and 27 seconds. The accuracy results were as follows: F1 macro: 0.866, F1 micro: 0.869, competition website on Codalab: 0.8468.

Due to the existence of text data, multi-label (ML) text classification is an essential task in machine learning. Feature selection is an essential and effective preprocess to enhance the learning process. Choosing a Multi-Label Feature Selection (MLFS) algorithm is the most basic, critical, and sensitive choice in ML classification operations. If this choice is based on a criterion, it cannot be attributed to always being sound. Choosing the best algorithm must be evaluated using several different criteria to be examined from different aspects. In this article, we turn the issue into an election and use the Weighted Borda Count method for voting. We do the voting in three stages continuously so that a subset of different features does the voting. In the second stage, voting of different methods is done with six criteria, and each criterion selects the methods in order of priority from the beginning to the end. Voting steps 1 and 2 are performed on eighteen text datasets used. Finally, in the final voting stage, the methods are evaluated and voted on by different text datasets. The final result of the voting in the third stage shows the desired MLFS methods based on their performance from beginning to end. According to the experiments performed and the results obtained, it can be seen that the selection of the algorithm based on several different criteria and considering the overall performance of the algorithm will be better than the selection based on one criterion.