Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Robustness Certificates Against Adversarial Examples for ReLU Networks
While neural networks have achieved high performance in different learning tasks, their accuracy drops significantly in the presence of small adversarial perturbations to inputs. Defenses based on regularization and adve…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification
The aim of this paper was to compare soft confusion matrix approach and Bayes metaclassifier under the multi-label classification framework. Although the methods were successfully applied under the multi-label classifica…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONStochastic Gradient Trees
We present an algorithm for learning decision trees using stochastic gradient information as the source of supervision. In contrast to previous approaches to gradient-based tree learning, our method operates in the incre…
ClassificationGeneral ClassificationIncremental LearningMulti-Label Classification+1Multi-Label Adversarial Perturbations
Adversarial examples are delicately perturbed inputs, which aim to mislead machine learning models towards incorrect outputs. While most of the existing work focuses on generating adversarial perturbations in multi-class…
General Classificationimage-classificationImage ClassificationMulti-class Classification+4Plugin Networks for Inference under Partial Evidence
In this paper, we propose a novel method to incorporate partial evidence in the inference of deep convolutional neural networks. Contrary to the existing, top performing methods, which either iteratively modify the input…
Multi-Label ClassificationScene RecognitionSemantic SegmentationA Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome
Adversarial examples pose many security threats to convolutional neural networks (CNNs). Most defense algorithms prevent these threats by finding differences between the original images and adversarial examples. However,…
General ClassificationMulti-Label ClassificationGroup Preserving Label Embedding for Multi-Label Classification
Multi-label learning is concerned with the classification of data with multiple class labels. This is in contrast to the traditional classification problem where every data instance has a single label. Due to the exponen…
ClassificationDimensionality Reductionfeature selectionGeneral Classification+3COSINE: Compressive Network Embedding on Large-scale Information Networks
There is recently a surge in approaches that learn low-dimensional embeddings of nodes in networks. As there are many large-scale real-world networks, it's inefficient for existing approaches to store amounts of paramete…
General Classificationgraph partitioningLink PredictionMulti-Label Classification+3Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible an…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONELASTIC: Improving CNNs with Dynamic Scaling Policies
Scale variation has been a challenge from traditional to modern approaches in computer vision. Most solutions to scale issues have a similar theme: a set of intuitive and manually designed policies that are generic and f…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemantic SegmentationLNEMLC: Label Network Embeddings for Multi-Label Classification
Multi-label classification aims to classify instances with discrete non-exclusive labels. Most approaches on multi-label classification focus on effective adaptation or transformation of existing binary and multi-class l…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advo…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label classification search space in the MEKA software
This supplementary material aims to describe the proposed multi-label classification (MLC) search spaces based on the MEKA and WEKA softwares. First, we overview 26 MLC algorithms and meta-algorithms in MEKA, presenting …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDeep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated sys…
Deep LearningDiagnosticMulti-Label ClassificationML-Net: multi-label classification of biomedical texts with deep neural networks
In multi-label text classification, each textual document can be assigned with one or more labels. Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the b…
BenchmarkingClassificationFeature EngineeringGeneral Classification+5SeCSeq: Semantic Coding for Sequence-to-Sequence based Extreme Multi-label Classification
Extreme multi-label classification (XMC) aims at assigning to an instance the most relevant subset of labels from a colossal label set. There has been some success in formulating the multi-label problem as sequence-to-se…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAdversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification
Recent work has shown that exploiting relations between labels improves the performance of multi-label classification. We propose a novel framework based on generative adversarial networks (GANs) to model label dependenc…
ClassificationGeneral Classificationimage-classificationImage Classification+3Automated Multi-Label Classification based on ML-Plan
Automated machine learning (AutoML) has received increasing attention in the recent past. While the main tools for AutoML, such as Auto-WEKA, TPOT, and auto-sklearn, mainly deal with single-label classification and regre…
AutoMLBIG-bench Machine LearningClassificationGeneral Classification+3Block-wise Partitioning for Extreme Multi-label Classification
Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large label set. Many existing solutions embed the label matrix to a low-dimensi…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+1Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages the power of convolutional recurrent ne…
Event DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION