Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
A Three-phase Augmented Classifiers Chain Approach Based on Co-occurrence Analysis for Multi-Label Classification
As a very popular multi-label classification method, Classifiers Chain has recently been widely applied to many multi-label classification tasks. However, existing Classifier Chains methods are difficult to model and exp…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAre We Really Making Much Progress in Text Classification? A Comparative Review
We analyze various methods for single-label and multi-label text classification across well-known datasets, categorizing them into bag-of-words, sequence-based, graph-based, and hierarchical approaches. Despite the surge…
ClassificationDecoderMulti-Label ClassificationMulti Label Text Classification+3Constrained Sequence-to-Tree Generation for Hierarchical Text Classification
Hierarchical Text Classification (HTC) is a challenging task where a document can be assigned to multiple hierarchically structured categories within a taxonomy. The majority of prior studies consider HTC as a flat multi…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONtext-classification+1Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE
We provide an end-to-end Renyi DP based-framework for differentially private top-$k$ selection. Unlike previous approaches, which require a data-independent choice on $k$, we propose to privately release a data-dependent…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor
Tree-ensemble algorithms, such as random forest, are effective machine learning methods popular for their flexibility, high performance, and robustness to overfitting. However, since multiple learners are combined, they …
Binary ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEvolving Multi-Label Fuzzy Classifier
Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time. We propose an evolving multi-label fuz…
Active LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONIntegrative Few-Shot Learning for Classification and Segmentation
We introduce the integrative task of few-shot classification and segmentation (FS-CS) that aims to both classify and segment target objects in a query image when the target classes are given with a few examples. This tas…
ClassificationFew-Shot Classification and SegmentationFew-Shot LearningFew-Shot Semantic Segmentation+3Deep Hierarchical Semantic Segmentation
Humans are able to recognize structured relations in observation, allowing us to decompose complex scenes into simpler parts and abstract the visual world in multiple levels. However, such hierarchical reasoning ability …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSegmentationSemantic SegmentationFeature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification
Gene annotation addresses the problem of predicting unknown associations between gene and functions (e.g., biological processes) of a specific organism. Despite recent advances, the cost and time demanded by annotation p…
ClusteringHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Deep-Discrete Learning Framework for Spherical Surface Registration
Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches. Classically, image registration is perfo…
Image RegistrationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks
Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label class…
ClassificationHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Academic Resource Text Level Multi-label Classification based on Attention
Hierarchical multi-label academic text classification (HMTC) is to assign academic texts into a hierarchically structured labeling system. We propose an attention-based hierarchical multi-label classification algorithm o…
ClassificationDocument EmbeddingHierarchical Multi-label ClassificationMulti-Label Classification+3Efficient Classification of Long Documents Using Transformers
Several methods have been proposed for classifying long textual documents using Transformers. However, there is a lack of consensus on a benchmark to enable a fair comparison among different approaches. In this paper, we…
ClassificationDocument ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSpeaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios
Overlapping speech diarization has been traditionally treated as a multi-label classification problem. In this paper, we reformulate this task as a single-label prediction problem by encoding multiple binary labels into …
Action DetectionActivity DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting
In document classification for, e.g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence of real world events, e.g…
Document ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSpatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations
As natural images usually contain multiple objects, multi-label image classification is more applicable "in the wild" than single-label classification. However, exhaustively annotating images with every object of interes…
Data Augmentationimage-classificationImage ClassificationMulti-Label Classification+2Font Shape-to-Impression Translation
Different fonts have different impressions, such as elegant, scary, and cool. This paper tackles part-based shape-impression analysis based on the Transformer architecture, which is able to handle the correlation among l…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTranslationRankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation
The segmentation task has traditionally been formulated as a complete-label pixel classification task to predict a class for each pixel from a fixed number of predefined semantic categories shared by all images or videos…
ClassificationInstance SegmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+6Graph Attention Transformer Network for Multi-Label Image Classification
Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize such inter-label correlations or dependenc…
ClassificationGraph Attentionimage-classificationImage Classification+3Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification
Hierarchical text classification is a challenging subtask of multi-label classification due to its complex label hierarchy. Existing methods encode text and label hierarchy separately and mix their representations for cl…
ClassificationContrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2