Single-label Classification Research Articles

Multi-Self-Attention for Aspect Category Detection and Biomedical Multilabel Text Classification with BERT

Multilabel classification is one of the most challenging tasks in natural language processing, posing greater technical difficulties than single-label classification. At the same time, multilabel classification has more natural applications. For individual labels, the whole piece of text has different focuses or component distributions, which require full use of local information of the sentence. As a widely adopted mechanism in natural language processing, attention becomes a natural choice for the issue. This paper proposes a multilayer self-attention model to deal with aspect category and word attention at different granularities. Combined with the BERT pretraining model, it achieves competitive performance in aspect category detection and electronic medical records’ classification.

Multi-label classification of research articles using Word2Vec and identification of similarity threshold

Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%.

Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence

In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, instead of selecting a single class label, predictions take the form of a subset of all labels. In this paper, we study an extension of the setting of MLC, in which the learner is allowed to partially abstain from a prediction, that is, to deliver predictions on some but not necessarily all class labels. This option is useful in cases of uncertainty, where the learner does not feel confident enough on the entire label set. Adopting a decision-theoretic perspective, we propose a formal framework of MLC with partial abstention, which builds on two main building blocks: First, the extension of underlying MLC loss functions so as to accommodate abstention in a proper way, and second the problem of optimal prediction, that is, finding the Bayes-optimal prediction minimizing this generalized loss in expectation. It is well known that different (generalized) loss functions may have different risk-minimizing predictions, and finding the Bayes predictor typically comes down to solving a computationally complexity optimization problem. In the most general case, given a prediction of the (conditional) joint distribution of possible labelings, the minimizer of the expected loss needs to be found over a number of candidates which is exponential in the number of class labels. We elaborate on properties of risk minimizers for several commonly used (generalized) MLC loss functions, show them to have a specific structure, and leverage this structure to devise efficient methods for computing Bayes predictors. Experimentally, we show MLC with partial abstention to be effective in the sense of reducing loss when being allowed to abstain.

Bioinspired Visual-Integrated Model for Multilabel Classification of Textile Defect Images

In modern textile industrial processes, textile defect recognition and classification are of vital importance for textile quality control. Recently, as the critical machine-learning method, the deep convolutional neural network (CNN) has shown superior performance in the single-label classification of textile defect images. However, accurately recognizing and classifying multilabel textile defect images are still an unsolved issue due to the complexity of intersected defects, as well as difficulty in distinguishing small-size defects and concerning the correlations amongst labels. To address these challenges, we propose a bioinspired visual-integrated model for multilabel classification of textile defects called BIVI-ML. Three bioinspired visual mechanisms (the visual gain mechanism, the visual attention mechanism, and the visual memory mechanism) are proposed and built within the BIVI-ML to: 1) enhance the resolution and feature discrimination; 2) attend to the textile defect; and 3) associate relevant labels. To evaluate the proposed method, a unique multilabel textile defect database is built as the benchmark for the multilabel classification of textile defect images. Compared with both the traditional and the state-of-the-art deep-learning classification methods, the proposed BIVI-ML achieves the best performance in both single-label and multilabel image classification.

Harnessing Multi-label Classification Approaches for Economic Phenomena Categorization

One fashion to report a country’s economic state is by compiling economic phenomena from several sources. The collected data may be explored based on their sentiments and economic categories. This research attempted to perform and analyze multiple approaches to multi-label text classification in addition to providing sentiment analysis on the economic phenomena. The sentiment and single-label category classification was performed utilizing the logistic regression model. Meanwhile, the multi-label category classification was fulfilled using a combination of logistic regression, support vector machines, k-nearest neighbor, naïve Bayes, and decision trees as base classifiers, with binary relevance, classifier chain, and label power set as the implementation approaches. The results showed that logistic regression works well in sentiment and single-label classification, with a classification accuracy of 80.08% and 92.71%, respectively. However, it was also discovered that it works poorly as a base classifier in multi-label classification, indicated by the classification accuracy dropping to 13.35%, 15.40%, and 30.65% for binary relevance, classifier chain, and label power set, respectively. Alternatively, naïve Bayes works best as a base classifier in the label power set approach for multi-label classification, with a classification accuracy of 63.22%, followed by decision trees and support vector machines.

Detecting Multi-label emotions from code-mixed Facebook Status Updates

Objectives: With the growth of Social Media and the increasing use of English-Hindi (Hinglish) in linguistically diverse countries such as India, it is becoming increasingly important to analyse Hinglish-language content on Social Media platforms like Facebook. Prior sentiment and emotion analyses have only focused on single-label classification, ignoring the possibility of coexisting emotions within one instance. By analysing code-mixed Facebook status updates, the study aims to investigate multiple emotions. Method: 15,995 English-Hindi mixed Facebook status updates are annotated with emotions like joy, sadness, anger, fear, trust, disgust, surprise, anticipation, and love. Different pre-processing techniques are used to normalize the noisy data to produce more accurate results. We apply five different multi-level classification algorithms with word-level and character n-gram approaches to test the best classification results. Findings: The results of the experiment indicate that a status update can evoke multiple emotions rather than just one. Precision, recall, F1 score, and accuracy using both Micro and Macro averaging are used to evaluate the performance of different classifiers. As compared to other classification algorithms, the Classifier Chains algorithm with its 2-6-gram approach has the highest accuracy of 86% with a precision of 0.98. As compared to other classifiers, the Classifier Chains algorithm offered better results due to its ability to consider the correlations between class labels. Applications: The article focuses on the multi-label emotion classification task, which examines whether a Facebook status update shows none, one, or more of the nine emotions as outlined by Plutchik’s wheel of emotions. Considering the emotion of a text can support decision-making processes in various ways. Keywords: Social Media; Emotion Analysis; Code-Mixed; MultiLabel 29 Classification; Emojis; Lexicons

Multi-Label Classification of Research Papers Using Multi-Label K-Nearest Neighbour Algorithm

With the frequent interaction and cooperation between different disciplines in recent years, the number of research papers associated with multiple subjects increased. Correspondingly, some of the existing literatures belong to a single discipline, while others may simultaneously involve more than 2 subjects. At this time, the traditional single-label text classification is not conducive to people obtaining comprehensive and cutting-edge research papers in real life. Thus, it’s of great importance to conduct a multi-label classification of research papers effectively. This paper tests the performance of multi-label learning tasks with text data obtained from the Kaggle website. Firstly, lemmatization and Term Frequency-Inverse Document Frequency (TF-IDF) are used for feature extraction in the pre-processing part. The critical information of text content is statistically analysed, and text content is converted into numerical and high-dimensional vector space. As the traditional single-label classification algorithm is not suitable for the above problem, this paper adopts the Multi-Label K-Nearest Neighbour (ML-KNN) algorithm framework for classification. Experimental results report that the ML-KNN algorithm has achieved better results in multi-label text classification problems than a traditional multi-label algorithm, which proves the effectiveness of the ML-KNN algorithm for text data prediction with multiple subjects. Moreover, the work in this paper is analysed and summarized.

CNN-based multi-class multi-label classification of sound scenes in the context of wind turbine sound emission measurements

Within the scope of the interdisciplinary project WEA-Akzeptanz, measurements of the sound emission of wind turbines were carried out at the Leibniz University Hannover. Due to the environment there are interfering components (e. g. traffic, birdsong, wind, rain, ...) in the recorded signals. Depending on the subsequent signal processing and analysis, it may be necessary to identify sections with the raw sound of a wind turbine, recordings with the purest possible background noise or even a specific combination of interfering noises. Due to the amount of data, a manual classification of the audio signals is usually not feasible and an automated classification becomes necessary. In this paper, we extend our previously proposed multi-class single-label classification model to a multi-class multi-label model, which reflects the real-world acoustic conditions around wind turbines more accurately and allows for finer-grained evaluations. We first provide a short overview of the data acquisition and the dataset. We then briefly summarize our previous approach, extend it to a multi-class multi-label formulation, and analyze the trained convolutional neural network regarding different metrics. All in all, the model delivers very reliable classification results with an overall example-based F1-score of about 80 % for a multi-label classification of 12 classes.

An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks

Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network representation learning methods have been proposed, where random walk strategy is one of the wildly utilized approaches. However, the existing random walk based methods have some challenges, including: 1. The insufficiency of explaining what network knowledge in the walking path-samplings; 2. The adverse effects caused by the mixture of different information in networks; 3. The poor generality of the methods with hyper-parameters on different networks. This paper proposes an information-explainable random walk based unsupervised network representation learning framework named Probabilistic Accepted Walk (PAW) to obtain network representation from the perspective of the stationary distribution of networks. In the framework, we design two stationary distributions based on nodes’ self-information and local-information of networks to guide our proposed random walk strategy to learn representational vectors of networks through sampling paths of nodes. Numerous experimental results demonstrated that the PAW could obtain more expressive representation than the other six widely used unsupervised network representation learning baselines on four real-world networks in single-label and multi-label node classification tasks.

An experimental approach for prediction of multi-classification using SVM

The multiclass classification problem is an important topic in the field of pattern recognition. It involves the task of classifying input instances into one of multiple classes. Since the class overlapping problem exists among multiple classes in most real-world problems, the multiclass classification task is much more complicated and challenging compared to the binary class problem. Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and multi-label classification. Traditional binary and multi-class classifications are subcategories of single-label classification. The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems. The results obtained are compared with state-of-the-art techniques from each of the classification types.

To what extent do DNN-based image classification models make unreliable inferences?

Deep Neural Network (DNN) models are widely used for image classification. While they offer high performance in terms of accuracy, researchers are concerned about if these models inappropriately make inferences using features irrelevant to the target object in a given image. To address this concern, we propose a metamorphic testing approach that assesses if a given inference is made based on irrelevant features. Specifically, we propose two metamorphic relations (MRs) to detect such unreliable inferences. These relations expect (a) the classification results with different labels or the same labels but less certainty from models after corrupting the relevant features of images, and (b) the classification results with the same labels after corrupting irrelevant features. The inferences that violate the metamorphic relations are regarded as unreliable inferences. Our evaluation demonstrated that our approach can effectively identify unreliable inferences for single-label classification models with an average precision of 64.1% and 96.4% for the two MRs, respectively. As for multi-label classification models, the corresponding precision for MR-1 and MR-2 is 78.2% and 86.5%, respectively. Further, we conducted an empirical study to understand the problem of unreliable inferences in practice. Specifically, we applied our approach to 18 pre-trained single-label image classification models and 3 multi-label classification models, and then examined their inferences on the ImageNet and COCO datasets. We found that unreliable inferences are pervasive. Specifically, for each model, more than thousands of correct classifications are actually made using irrelevant features. Next, we investigated the effect of such pervasive unreliable inferences, and found that they can cause significant degradation of a model’s overall accuracy. After including these unreliable inferences from the test set, the model’s accuracy can be significantly changed. Therefore, we recommend that developers should pay more attention to these unreliable inferences during the model evaluations. We also explored the correlation between model accuracy and the size of unreliable inferences. We found the inferences of the input with smaller objects are easier to be unreliable. Lastly, we found that the current model training methodologies can guide the models to learn object-relevant features to certain extent, but may not necessarily prevent the model from making unreliable inferences. We encourage the community to propose more effective training methodologies to address this issue.

A Real-Time Image Semantic Segmentation Method Based on Multilabel Classification

Image semantic segmentation as a kind of technology has been playing a crucial part in intelligent driving, medical image analysis, video surveillance, and AR. However, since the scene needs to infer more semantics from video and audio clips and the request for real-time performance becomes stricter, whetherthe single-label classification method that was usually used before or the regular manual labeling cannot meet this end. Given the excellent performance of deep learning algorithms in extensive applications, the image semantic segmentation algorithm based on deep learning framework has been brought under the spotlight of development. This paper attempts to improve the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) based on the multilabel classification method by the following steps. First, the standard convolution is replaced by applying Receptive Field in Deep Convolutional Neural Network in the convolution layer, to the extent that every pixel in the covered area would facilitate the ultimate feature response. Second, the ASPP (Atrous Spatial Pyramid Pooling) module is improved based on the atrous convolution, and the DB-ASPP (Delate Batch Normalization-ASPP) is proposed as a way to reducing gridding artifacts due to the multilayer atrous convolution, acquiring multiscale information, and integrating the feature information in relation to the image set. Finally, the proposed model and regular models are subject to extensive tests and comparisons on a plurality of multiple data sets. Results show that the proposed model demonstrates a good accuracy of segmentation, the smallest network parameter at 0.3 M and the fastest speed of segmentation at 25 FPS.

Condition-CNN: A hierarchical multi-label fashion image classification model

Current state of the art image classifiers predict a single class label of an image. However, in many industry settings such as online shopping, images belong to a class hierarchy where the first level represents the coarse grained or the most abstract class with subsequent levels representing the more specific classes. We propose a novel hierarchical image classification model, Condition-CNN, which addresses some of the shortcomings of the branching convolutional neural network in terms of training time and fine-grained accuracy. It applies the Teacher Forcing training algorithm, where the actual class labels of the higher level classes rather than the predicted labels are used to train the lower level branches. The technique also prevents error propagation, and thereby, reduces the training time. Besides learning the image features for each level of classes, Condition-CNN also learns the relationship between different levels of classes as conditional probabilities, which is used to estimate class predictions during scoring. By feeding the estimated higher-level class predictions as priors to the lower-level class prediction, Condition-CNN achieves a superior prediction accuracy while requiring fewer trainable parameters compared to the baseline CNN models. The validation results of Condition-CNN using the Kaggle Fashion Product Images data set demonstrate a prediction accuracy of 99.8%, 98.1%, and 91.0% for Level 1, 2 and 3 classes respectively, which are greater than that of B-CNN and other baseline CNN models. Moreover, Condition-CNN used only 77.58% of the total number of trainable parameters as that of B-CNN.

Efficient set-valued prediction in multi-class classification

In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision (the candidates are not too many) of its prediction. We formalize this problem within a general decision-theoretic framework that unifies most of the existing work in this area. In this framework, uncertainty is quantified in terms of conditional class probabilities, and the quality of a predicted set is measured in terms of a utility function. We then address the problem of finding the Bayes-optimal prediction, i.e., the subset of class labels with the highest expected utility. For this problem, which is computationally challenging as there are exponentially (in the number of classes) many predictions to choose from, we propose efficient algorithms that can be applied to a broad family of utility functions. Our theoretical results are complemented by experimental studies, in which we analyze the proposed algorithms in terms of predictive accuracy and runtime efficiency.

A Multilabel Classifier for Text Classification and Enhanced BERT System

Now-a-day, a vast variety of reviews are published on the web. As a result, an automated system to analyze and extract knowledge from such textual data is needed. Sentiment analysis is a well-known sub-area in Natural Language Processing (NLP). In earlier research, sentiments were determined without considering the aspects specified in a review instance. Aspect-based sentiment analysis (ABSA) has caught the attention of researchers. Many existing systems consider ABSA as a single label classification problem. This drawback is handled in this study by proposing three approaches that use multilabel classifiers for classification. In the first approach, the performance of a model with hybrid features is analyzed using the multilabel classifier. The hybrid feature set includes word dependency rule-based features and unigram features selected using the proposed two-phase weighted correlation feature selection (WCFS) approach. In the second and third approaches Bidirectional Encoder Representation from Transformers (BERT) language model is used. In the second approach, a BERT system is enhanced by applying max pooling on target terms which specify an aspect of a review instance and a multibit label is given as input to the BERT system. In the third approach, the basic BERT system is used for word embedding only and classification is done using multilabel classifiers. In all approaches, the label used for all training instances specifies aspects with its sentiments. The experimentation shows that the results gained using the system proposed in the first approach are comparable to the results gained using the BERT system. The experimental results depict that the Enhanced BERT system gives better results compared to the existing systems.

Rotation Forest for multi-target regression

The prediction of multiple numeric outputs at the same time is called multi-target regression (MTR), and it has gained attention during the last decades. This task is a challenging research topic in supervised learning because it poses additional difficulties to traditional single-target regression (STR), and many real-world problems involve the prediction of multiple targets at once. One of the most successful approaches to deal with MTR, although not the only one, consists in transforming the problem in several STR problems, whose outputs will be combined building up the MTR output. In this paper, the Rotation Forest ensemble method, previously proposed for single-label classification and single-target regression, is adapted to MTR tasks and tested with several regressors and data sets. Our proposal rotates the input space in an efficient and novel fashion, avoiding extra rotations forced by MTR problem decomposition. Four approaches for MTR are used: single-target (ST), stacked-single target (SST), Ensembles of Regressor Chains (ERC), and Multi-target Regression via Quantization (MRQ). For assessing the benefits of the proposal, a thorough experimentation with 28 MTR data sets and statistical tests are used, concluding that Rotation Forest, adapted by means of these approaches, outperforms other popular ensembles, such as Bagging and Random Forest.

Research on Multiple Classification Based on Improved SVM Algorithm for Balanced Binary Decision Tree

Support vector machines (SVMs) are designed to solve the binary classification problems at the beginning, but in the real world, there are a lot of multiclassification cases. The multiclassification methods based on SVM are mainly divided into the direct methods and the indirect methods, in which the indirect methods, which consist of multiple binary classifiers integrated in accordance with certain rules to form the multiclassification model, are the most commonly used multiclassification methods at present. In this paper, an improved multiclassification algorithm based on the balanced binary decision tree is proposed, which is called the IBDT-SVM algorithm. In this algorithm, it considers not only the influence of “between-classes distance” and “class variance” in traditional measures of between-classes separability but also takes “between-classes variance” into consideration and proposes a new improved “between-classes separability measure.” Based on the new “between-classes separability measure,” it finds out the two classes with the largest between-classes separability measure and uses them as the positive and negative samples to train and learn the classifier. After that, according to the principle of the class-grouping-by-majority, the remaining classes are close to these two classes and merged into the positive samples and the negative samples to train SVM classifier again. For the samples with uneven distribution or sparse distribution, this method can avoid the error caused by the shortest canter distance classification method and overcome the “error accumulation” problem existing in traditional binary decision tree to the greatest extent so as to obtain a better classifier. According to the above algorithm, each layer node of the decision tree is traversed until the output classification result is a single-class label. The experimental results show that the IBDT-SVM algorithm proposed in this paper can achieve better classification accuracy and effectiveness for multiple classification problems.

Sentiment analysis of GO-JEK services quality using Multi-Label Classification

One of the most popular online transportation providers in Indonesia is GO-JEK. At the first establishment, GO-JEK is only an online taxi motorbike service (in Bahasa Indonesia: ojek) that transforms from a conventional taxi motorbike. After several years GO-JEK began to develop more services, like GO-FOOD, GO-SEND, GO-CAR, GO-MART, GO-RIDE, GO-PAY, GO-TIX, GO-BOX, and GO-MED. As the GO-JEK services develop into more categories, it is more challenging to automatically analyze the sentiment polarity for each category of services category. An ordinary classification algorithm or single-label classification is concerned with learning from a set of examples associated with a single label classification. However, in this case, we want to classify GO-JEK services based on two-class targets, which are GO-JEK service categories and polarity sentiment classification. This research methodology contains Dataset Preparation, Feature Selection, Basic text mining process, train, and split dataset, and Classification. We implemented two classification methods: Multi-Label Classification and a simultaneous classification using the Random Forest Algorithm as a comparison. Based on this dataset, the most mentioned GO-JEK service is GO-FOOD followed by GO-SEND, GO-RIDE, GO-CAR, and GO-MART. Based on the service category, GO-FOOD gets the most positive reviews following by GO-RIDE and GOJEK 90. Some service categories like GO-SEND and GO-MART get more negative reviews than positive reviews. The accuracy of the Multi-label classification method raised 76%. Simultaneous classification accuracy using the Random Forest algorithm produce for service category classification yields 97% and only 78% for sentiment polarity classification. We can see that both algorithm, multi-label classification, and Random Forest algorithm, yields almost the same classification accuracy for polarity sentiment classification. We can conclude that the imperfect accuracy in polarity sentiment classification is related to the difficulty of identifying the most suitable polarity for each tweet. Many sarcasm tweets and slang words that cannot be identified.

A new fusion framework for motion segmentation in dynamic scenes

ABSTRACT Motion segmentation in dynamic scenes is currently widely dominated by parametric methods based on deep neural networks. The present study explores the unsupervised segmentation approach that can be used in the absence of training data to segment new videos. In particular, it tackles the task of dynamic texture segmentation. By automatically assigning a single class label to each region or group, this task consists of clustering into groups complex phenomena and characteristics which are both spatially and temporally repetitive. We present an effective fusion framework for motion segmentation in dynamic scenes (FFMS). This model is designed to merge different segmentation maps that contain multiple and weak quality regions in order to achieve a more accurate final result of segmentation. The diverse labelling fields required for the combination process are obtained by a simplified grouping scheme applied to an input video (on the basis of a three orthogonal planes: , and ). Experiments conducted on two challenging datasets (SynthDB and YUP++) show that, contrary to current motion segmentation approaches that either require parameter estimation or a training step, FFMS is significantly faster, easier to code, simple and has limited parameters.

An intelligent work order classification model for government service based on multi-label neural network

The classification of work orders for livelihood service play an important role in the e-government system, which goal is to push these work orders to the corresponding processing department. Although many studies have realized the classification of work orders automatically, these work orders are either related to many subjects or related to many departments. Therefore, the problem of work order classification is not a single label classification problem, but a multi-label classification problem. In view of the continuous development of deep neural networks, the goal of this paper is to modify a MultilabelMarginLoss function and design a MultilabelCrossEntropyLoss function to realize the multi-classification system of work orders, which can push the work orders to the related departments(one or more departments) automatically according to their appeal contents (consisting of short texts). We perform deep neural network for text classification including TextCNN, TextRCNN, TextRNN, TextRNN_Att, FastText and DPCNN on the real government hotline data set and compare the performances of these models. The results reveal that TextCNN and TextRCNN have a more stable performance on different loss functions, especially on the MultilabelCrossEntropyLoss function.