Missing labels are common in multi-label learning and can bias both label-correlation estimation and classifier induction. Existing missing-label methods often recover incomplete supervision mainly through global label correlations. However, correlat...
Multi-Label Classification refers to the classification task where a data sample is associated with multiple labels simultaneously, which is widely used in text classification, image classification, and other fields. Different from the traditional si...
Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representi...
Carbon labels are considered a fundamental tool for reducing emissions associated with grocery products. Although the prior literature has shown that both limited motivation and understanding of carbon labels explain the effectiveness of carbon label...
Existing feature selection methods mainly target single-label learning and multi-label learning, and only a few algorithms are optimized for label distribution learning. In label distribution learning, the associated labels of each sample have differ...
Data representation is of significant importance in minimizing multi-label ambiguity. While most researchers intensively investigate label correlation, the research on enhancing model robustness is preliminary. Low-quality data is one of the main rea...
We propose using five data-driven community detection approaches from social networks to partition the label space in the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd. We evalua...
Multi-label feature selection, which aims to select reliable and information-rich feature subsets from high-dimensional multi-label data, plays a critical role in data mining and pattern recognition. Conventional information-theoretic methods approxi...
Since the fabrication of the first electrochemical biosensor by Leland C. Clark in 1956, various labeled and label-free sensors have been reported for the detection of biomolecules. Labels such as nanoparticles, enzymes, Quantum dots, redox-active mo...
Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning. Feature selection plays an important role in multi-label learning to address multi-label dat...
Weak and transient protein interactions are involved in dynamic biological responses and are an important research subject; however, methods to elucidate such interactions are lacking. Proximity labeling is a promising technique for labeling transien...
Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins rem...
Protein microarray technology has gone through numerous innovative developments in recent decades. In this review, we focus on the development of protein detection methods embedded in the technology. Early microarrays utilized useful chromophores and...
Partial multi-label learning (PML) assigns each instance a candidate label set that contains all relevant labels but may also include irrelevant noisy ones, making reliable disambiguation essential. Although a small number of verified clean labels is...
Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a mu...
A variety of labelings on trees have emerged in order to attack the Graceful Tree Conjecture, but lack showing the connections between two labelings. In this paper, we propose two new labelings: vertex image-labeling and edge image-labeling, and comb...
Many aerial images with similar appearances have different but correlated scene labels, which causes the label ambiguity. Label distribution learning (LDL) can express label ambiguity by giving each sample a label distribution. Thus, a sample contrib...
Multi-label node classification requires capturing complex label-co-occurrence patterns, which are critical for accurate predictions. However, traditional methods often treat label prediction as an independent task, overlooking the semantic relations...
The literature on the effect of organic labels on consumers’ perception of food products has grown significantly over the last two decades. Since the number of empirical studies has also increased greatly, a literature review revealing the oper...
In multi-label data, a sample is associated with multiple labels at the same time, and the computational complexity is manifested in the high-dimensional feature space as well as the interdependence and unbalanced distribution of labels, which leads...
Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in...
Due to the exceptional learning capabilities of deep neural networks (DNNs), they continue to struggle to handle label noise. To address this challenge, the pseudo-label approach has emerged as a preferred solution. Recent works have achieved signifi...
Multi-label remote sensing scene classification requires identifying multiple land-cover categories from a single high-resolution aerial image. Existing methods strengthen visual features or model label dependencies, yet they apply a fixed calibratio...
In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other. As we all know, exploiting label correlations can definitely improve the...
The proliferation of quality labels for the same food product questions the relevance of labeling schemes. Based on the theory of legitimacy and research on food-related consumer behavior, this study aims to examine the influence of the perceived leg...
Multi-label streaming feature selection has received widespread attention in recent years because the dynamic acquisition of features is more in line with the needs of practical application scenarios. Most previous methods either assume that the labe...
To help consumers understand the healthfulness of food and make healthy food choices, manufacturers are increasingly providing front-of-package nutrition labels. However, not all types of front-of-package nutrition labels can promote consumers’...
We aimed to compare New Zealand private label (PL) and branded label (BL) packaged food products in relation to their current (2019) healthiness (sodium and sugar contents, and estimated Health Star Rating (HSR) score), display of the voluntary HSR n...
A bijective product k-cordial labeling f of a graph G with vertex set V and edge set E is a bijection from V to
The effectiveness of sugar labeling depends not only on direct sugar reduction but also on the extent to which compensatory eating occurs. This study focuses on the use of sucrose grade labels in the Chinese market to investigate not only consumers&r...
The paper aims to identify selected predictors of food label use to extend our knowledge about consumer behavior related to food purchases. Two types of information were examined: front-of-package (FOP) and back-of-package (BOP), and two contexts of...
Multi-label text classification (MLTC) aims to assign appropriate labels to each document from a given set. Prior research has acknowledged the significance of label information, but its utilization remains insufficient. Existing approaches often foc...
Weakly labeled sound event detection (WSED) is an important task as it can facilitate the data collection efforts before constructing a strongly labeled sound event dataset. Recent high performance in deep learning-based WSED’s exploited using...
NASA’s ICESat-2 space-borne photon-counting lidar mission is providing global elevation measurements that will provide significant benefits to a variety of ecosystem related research applications. Given the novelty of elevation and the derived...
The concepts of k-product cordial labeling and edge product cordial labeling were introduced in 2012 and further explored by various researchers. Building on these ideas, we define a new concept called ‘edge k-product cordial labeling’ as...
Objective: This study addresses multiclass industrial fault classification when human labels are simultaneously scarce and class-imbalanced. The proposed hierarchical balanced pseudo-label random forest (HBPL-RF) is novel in coordinating class-adapti...
Multi-label text classification (MLTC) aims to assign the most appropriate label or labels to each input text. Previous studies have focused on mining textual information, ignoring the interdependence of labels and texts, thus leading to the loss of...
In the domain of multi-label classification, label correlations play a crucial role in enhancing prediction precision. However, traditional methods heavily depend on ground-truth label sets, which can be incompletely tagged due to the diverse backgro...
Current food production and consumption patterns pose serious threats to our global environment. The goal of the current research is to investigate whether the presence of specific informational cues, i.e., ecological labels and nutrition labels, can...
Annotated text datasets are increasingly reused as classifier targets, annotation candidates, and inputs to aggregate profiles, yet their labels often circulate without enough information about how they were produced. This article presents a reproduc...
Graph labeling is the process of assigning labels to vertices and edges under certain conditions. This paper investigates the graceful local antimagic labeling of various graph families, excluding symmetric labelings, using computational experiments...
Background: Brazil is currently debating the implementation of front-of-package labels. This study tested if Warning labels (WLs) improved consumer understanding, perceptions, and purchase intentions compared to Traffic-Light labels (TLLs) in 1607 Br...
There is an identified need for point-of-care diagnostic systems for detecting and counting specific rare types of circulating cells in blood. By adequately labeling such cells with immunomagnetic beads and quantum dots, they can be efficiently colle...
Food labels allow producers to promote the unique selling points of their products and help consumers buy products with value-adding qualities. The aim of the present article is hence to explore Czech food producers’ expectations and experience...
Partial multi-label feature selection aims to identify discriminative features from data where each instance is associated with an ambiguous candidate label set. Existing methods are typically built upon single-scale modeling assumptions and may fail...
Photoaffinity labeling is a powerful technique for identifying a target protein. A high degree of labeling specificity can be achieved with this method in comparison to chemical labeling. Human serum albumin (HSA) and α1-acid glycoprotein (AGP) are t...
Multi-label text classification is a fundamental and important task in natural language processing, with widespread applications in specialized domains such as sentiment analysis, legal document classification, and medical coding. However, real-world...
Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life...
Radio mean labeling of a connected graph G is an assignment of distinct positive integers to the vertices of G satisfying a mathematical constraint called radio mean condition. The maximum label assigned to any vertex of G is called the
The potential for nutrition labels to impact on population health is dependent on consumer ability to understand and use this information. Consumer understanding of this information varies across sociodemographic groups and with different label desig...
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