Single-label Classification Task Research Articles

An artificial immune system algorithm for classification tasks. An electronic nose case study

This study explores the application of an electronic nose (e-nose), employing an Artificial Immune System (AIS) as a competitive alternative to the typical machine learning techniques. The proposed algorithm's main function is to modify its dimensions and adjust the values of its parameters during the training or mutation process, with no complex operations and minimal parameter tuning. This analytical tool has been applied to the qualitative analysis of chocolate bars, classifying varieties in single and multi-label scenarios. The classification results demonstrate promising results in accurately distinguishing individual chocolate types, obtaining an average accuracy of 97%. Subsequently, multi-label classification (several labels associated with a single chocolate) was executed based on shared ingredient content, achieving an average accuracy of 94%. The evaluation and validation of the proposed AIS were also verified; the obtained performance metrics were compared with those of typical classification techniques, k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Satisfactory results show the competitive performance of AIS in single-label classification tasks compared to named techniques. Promisingly, a novel method for multi-label learning in e-noses is obtained to accurately identify different types of chocolates, outperforming methods like KNN and SVM. These results demonstrate the AIS potential as an effective classification method for e-nose applications.

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.

A new Classifier Chain method of BERT Models For Multi-label Classification of Arabic Abusive Language on Social Media

Moving beyond classical single-label classification tasks proves to be crucial nowadays since the majority of real-life problems are inherently multi-label. Various tasks require involving, for one single instance as an input, several classes simultaneously or none of them. Particularly, detecting abusive texts in Arabic social networks intuitively carries several manifestations with regard to the targets’ referred characteristics like gender, race, sexual orientation, etc. Existing multi-label algorithms either invest considerable complexity to handle labels’ correlation or explicitly ignore this information to deal with each label as an independent binary problem. To remedy the abovementioned problems, this paper proposes a new multi-label classification solution based on deep neural networks. The main idea is to exploit the correlation between all the dataset's labels to break it into ordered subsets. Then, follow that order to implement a chain of BERT classifiers. Each classifier is therefore trained on a corresponding subset and passes its output via feature space to the following one. For a new instance, we gather all the classifiers’ results and pick the best one for each class. Experiments showed that our approach significantly outperformed the single classifier's method using BERT as well as other neural network architectures on our Arabic abusive language dataset. 91%,76%, and 82% were achieved respectively for precision, recall, and F1-score. Moreover, we further investigated the performance of our approach on Kaggle multi-label research paper dataset and achieved very promising results.

3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification

We examine a classification task in which signals of naturally occurring earthquakes are categorized ranging from minor to major, based on their magnitude. Generalized to a single-label classification task, most prior investigations have focused on assessing whether an earthquake’s magnitude falls into the minor or large categories. This procedure is often not practical since the tremor it generates has a wide range of variation in the neighboring regions based on the distance, depth, type of surface, and several other factors. We present an integrated 3-dimensional convolutional recurrent neural network (3D-CNN-RNN) trained to classify the seismic waveforms into multiple categories based on the problem formulation. Recent studies demonstrate using artificial intelligence-based techniques in earthquake detection and location estimation tasks with progress in collecting seismic data. However, less work has been performed in classifying the seismic signals into single or multiple categories. We leverage the use of a benchmark dataset comprising of earthquake waveforms having different magnitude and present 3D-CNN-RNN, a highly scalable neural network for multi-label classification problems. End-to-end learning has become a conventional approach in audio and image-related classification studies. However, for seismic signals classification, it has yet to be established. In this study, we propose to deploy the trained model on personal seismometers to effectively categorize earthquakes and increase the response time by leveraging the data-centric approaches. For this purpose, firstly, we transform the existing benchmark dataset into a series of multi-label examples. Secondly, we develop a novel 3D-CNN-RNN model for multi-label seismic event classification. Finally, we validate and evaluate the learned model with unseen seismic waveforms instances and report whether a specific event is associated with a particular class or not. Experimental results demonstrate the superiority and effectiveness of the proposed approach on unseen data using the multi-label classifier.

Seeking Subjectivity in Visual Emotion Distribution Learning.

Visual Emotion Analysis (VEA), which aims to predict people's emotions towards different visual stimuli, has become an attractive research topic recently. Rather than a single label classification task, it is more rational to regard VEA as a Label Distribution Learning (LDL) problem by voting from different individuals. Existing methods often predict visual emotion distribution in a unified network, neglecting the inherent subjectivity in its crowd voting process. In psychology, the Object-Appraisal-Emotion model has demonstrated that each individual's emotion is affected by his/her subjective appraisal, which is further formed by the affective memory. Inspired by this, we propose a novel Subjectivity Appraise-and-Match Network (SAMNet) to investigate the subjectivity in visual emotion distribution. To depict the diversity in crowd voting process, we first propose the Subjectivity Appraising with multiple branches, where each branch simulates the emotion evocation process of a specific individual. Specifically, we construct the affective memory with an attention-based mechanism to preserve each individual's unique emotional experience. A subjectivity loss is further proposed to guarantee the divergence between different individuals. Moreover, we propose the Subjectivity Matching with a matching loss, aiming at assigning unordered emotion labels to ordered individual predictions in a one-to-one correspondence with the Hungarian algorithm. Extensive experiments and comparisons are conducted on public visual emotion distribution datasets, and the results demonstrate that the proposed SAMNet consistently outperforms the state-of-the-art methods. Ablation study verifies the effectiveness of our method and visualization proves its interpretability.

Multi-label semantic feature fusion for remote sensing image captioning

For remote sensing image (RSI) captioning tasks, two-stage RSI captioning methods have achieved high performance because they introduce the results of other RSI tasks, such as image classification as prior information. However, most previous works treat image classification in the two-stage RSI captioning method as a single-label classification task, which cannot adequately describe the entire contents of complex RSIs and may cause semantic ambiguity. To settle this problem and further refine image feature representation, we introduce multi-label classification into two-stage RSI captioning to provide sufficient and accurate prior semantic information and propose a multi-label semantic feature fusion (MLSFF) framework. Specifically, we design a robust multi-label semantic attribute extractor to extract multi-label semantic attributes of RSIs. To obtain discriminative feature representations for RSI captioning, we propose two cross-modal semantic feature fusion operators that fuse the extracted semantic attributes and the image feature extracted by the convolutional neural network. The results of extensive numerical experiments show that the proposed method can achieve state-of-the-art performance on the UCM-Captions, Sydney-Captions, and RSICD datasets. Specifically, on the UCM-Captions dataset, our method achieves a gain of 8.2% in Sm score over the SAT (LAM) method (Zhang et al., 2019c). On the Sydney-Captions dataset, our method improves the Sm score by 17.4% compared with the TCE loss-based method (Li et al., 2020a). On the RSICD, our method outperforms the multi-level attention method (Li et al., 2020b) by 3.2% in terms of the Sm score. Code is available at https://github.com/xtye5025/MLSFF.

Graph convolutional networks with attention for multi-label weather recognition

Weather recognition is a significant technique for many potential computer vision applications in our daily lives. Generally, most existing works treat weather recognition as a single-label classification task, which cannot describe the weather conditions comprehensively due to the complex co-occurrence dependencies between different weather conditions. In this paper, we propose a novel Graph Convolution Networks with Attention (GCN-A) model for multi-label weather recognition. To our best knowledge, this is the first attempt to introduce GCN into weather recognition. Specifically, we employ GCN to capture weather co-occurrence dependencies via a directed graph. The graph is built over weather labels, where each node (weather label) is represented by word embeddings of a weather label. Furthermore, we design a re-weighted mechanism to build weather correlation matrix for information propagation among different nodes in GCN. In addition, we develop a channel-wise attention module to extract informative semantic features of weather for effective model training. Compared with the state-of-the-art methods, experiment results on two widely used benchmark datasets demonstrate that our proposed GCN-A model achieves promising performance.

Co-learning binary classifiers for LP-based multi-label classification

A simple yet practical multi-label learning method, called label powerset (LP), considers each different combination of labels that appear in the training set as a different class value of a single-label classification task. However, because those classes source from multiple labels, there may be some inherent relationships among them. To tackle this challenge, we propose a novel model which aims to co-learn binary classifiers, by combining the training of binary classifiers and the characterizing the relationship among them into a unified objective function. In addition, we develop an alternating optimization algorithm to solve the proposed problem. Extensive experimental results on various kinds of datasets well demonstrate the effectiveness of the proposed model.

A CNN–RNN architecture for multi-label weather recognition

Weather Recognition plays an important role in our daily lives and many computer vision applications. However, recognizing the weather conditions from a single image remains challenging and has not been studied thoroughly. Generally, most previous works treat weather recognition as a single-label classification task, namely, determining whether an image belongs to a specific weather class or not. This treatment is not always appropriate, since more than one weather conditions may appear simultaneously in a single image. To address this problem, we make the first attempt to view weather recognition as a multi-label classification task, i.e., assigning an image more than one labels according to the displayed weather conditions. Specifically, a CNN-RNN based multi-label classification approach is proposed in this paper. The convolutional neural network (CNN) is extended with a channel-wise attention model to extract the most correlated visual features. The Recurrent Neural Network (RNN) further processes the features and excavates the dependencies among weather classes. Finally, the weather labels are predicted step by step. Besides, we construct two datasets for the weather recognition task and explore the relationships among different weather conditions. Experimental results demonstrate the superiority and effectiveness of the proposed approach. The new constructed datasets will be available at https://github.com/wzgwzg/Multi-Label-Weather-Recognition.

AROMA

Human activity recognition (HAR) is a promising research issue in ubiquitous and wearable computing. However, there are some problems existing in traditional methods: 1) They treat HAR as a single label classification task, and ignore the information from other related tasks, which is helpful for the original task. 2) They need to predesign features artificially, which are heuristic and not tightly related to HAR task. To address these problems, we propose AROMA (human activity recognition using deep multi-task learning). Human activities can be divided into simple and complex activities. They are closely linked. Simple and complex activity recognitions are two related tasks in AROMA. For simple activity recognition task, AROMA utilizes a convolutional neural network (CNN) to extract deep features, which are task dependent and non-handcrafted. For complex activity recognition task, AROMA applies a long short-term memory (LSTM) network to learn the temporal context of activity data. In addition, there is a shared structure between the two tasks, and the object functions of these two tasks are optimized jointly. We evaluate AROMA on two public datasets, and the experimental results show that AROMA is able to yield a competitive performance in both simple and complex activity recognitions.

Incremental Approach of Neural Network in Back Propagation Algorithms for Web Data Mining

Text Classification is also called as Text Categorization (TC), is the task of classifying a set of text documents automatically into different categories from a predefined set. If a text document relates to exactly one of the categories, then it is called as single-label classification task; otherwise, it is called as multi-label classification task. For Information Retrieval (IR) and Machine Learning (ML), TC uses several tools and has received much attention in the last decades. In this paper, first classifies the text documents using MLP based machine learning approach (BPP) and then return the most relevant documents. And also describes a proposed back propagation neural network classifier that performs cross validation for original Neural Network. In order to optimize the classification accuracy, training time. Proposed web content mining methodology in the exploration with the aid of BPP. The main objective of this investigation is web document extraction and utilizing different grouping algorithm. This work extricates the data from the web URL.

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning.

Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC) features which represent a manually-designed summary of spectral information. However, recent work in machine learning has demonstrated that features learnt automatically from data can often outperform manually-designed feature transforms. Feature learning can be performed at large scale and “unsupervised”, meaning it requires no manual data labelling, yet it can improve performance on “supervised” tasks such as classification. In this work we introduce a technique for feature learning from large volumes of bird sound recordings, inspired by techniques that have proven useful in other domains. We experimentally compare twelve different feature representations derived from the Mel spectrum (of which six use this technique), using four large and diverse databases of bird vocalisations, classified using a random forest classifier. We demonstrate that in our classification tasks, MFCCs can often lead to worse performance than the raw Mel spectral data from which they are derived. Conversely, we demonstrate that unsupervised feature learning provides a substantial boost over MFCCs and Mel spectra without adding computational complexity after the model has been trained. The boost is particularly notable for single-label classification tasks at large scale. The spectro-temporal activations learned through our procedure resemble spectro-temporal receptive fields calculated from avian primary auditory forebrain. However, for one of our datasets, which contains substantial audio data but few annotations, increased performance is not discernible. We study the interaction between dataset characteristics and choice of feature representation through further empirical analysis.

KNN based Machine Learning Approach for Text and Document Mining

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents.

Random k-Labelsets for Multilabel Classification

A simple yet effective multilabel learning method, called label powerset (LP), considers each distinct combination of labels that exist in the training set as a different class value of a single-label classification task. The computational efficiency and predictive performance of LP is challenged by application domains with large number of labels and training examples. In these cases, the number of classes may become very large and at the same time many classes are associated with very few training examples. To deal with these problems, this paper proposes breaking the initial set of labels into a number of small random subsets, called labelsets and employing LP to train a corresponding classifier. The labelsets can be either disjoint or overlapping depending on which of two strategies is used to construct them. The proposed method is called RAkEL (RAndom k labELsets), where k is a parameter that specifies the size of the subsets. Empirical evidence indicates that RAkEL manages to improve substantially over LP, especially in domains with large number of labels and exhibits competitive performance against other high-performing multilabel learning methods.

Decision trees for hierarchical multi-label classification

Hierarchical multi-label classification (HMC) is a variant of classification where instances may belong to multiple classes at the same time and these classes are organized in a hierarchy. This article presents several approaches to the induction of decision trees for HMC, as well as an empirical study of their use in functional genomics. We compare learning a single HMC tree (which makes predictions for all classes together) to two approaches that learn a set of regular classification trees (one for each class). The first approach defines an independent single-label classification task for each class (SC). Obviously, the hierarchy introduces dependencies between the classes. While they are ignored by the first approach, they are exploited by the second approach, named hierarchical single-label classification (HSC). Depending on the application at hand, the hierarchy of classes can be such that each class has at most one parent (tree structure) or such that classes may have multiple parents (DAG structure). The latter case has not been considered before and we show how the HMC and HSC approaches can be modified to support this setting. We compare the three approaches on 24 yeast data sets using as classification schemes MIPS's FunCat (tree structure) and the Gene Ontology (DAG structure). We show that HMC trees outperform HSC and SC trees along three dimensions: predictive accuracy, model size, and induction time. We conclude that HMC trees should definitely be considered in HMC tasks where interpretable models are desired.