Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Learning to diagnose from scratch by exploiting dependencies among labels
The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into clas…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTranslationSimilarity-based Multi-label Learning
Multi-label classification is an important learning problem with many applications. In this work, we propose a principled similarity-based approach for multi-label learning called SML. We also introduce a similarity-base…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix
In this work we addressed the issue of applying a stochastic classifier and a local, fuzzy confusion matrix under the framework of multi-label classification. We proposed a novel solution to the problem of correcting lab…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Correction Method of a Binary Classifier Applied to Multi-label Pairwise Models
In this work, we addressed the issue of applying a stochastic classifier and a local, fuzzy confusion matrix under the framework of multi-label classification. We proposed a novel solution to the problem of correcting la…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONUsing Multi-Label Classification for Improved Question Answering
A plethora of diverse approaches for question answering over RDF data have been developed in recent years. While the accuracy of these systems has increased significantly over time, most systems still focus on particular…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Dynamic classifier chains for multi-label learning
In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of classifier chains algorithms that are able to change label or…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningLearning Social Image Embedding with Deep Multimodal Attention Networks
Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social …
ClassificationGeneral ClassificationLink PredictionMulti-Label Classification+2LASAGNE: Locality And Structure Aware Graph Node Embedding
In this work we propose Lasagne, a methodology to learn locality and structure aware graph node embeddings in an unsupervised way. In particular, we show that the performance of existing random-walk based approaches depe…
Link PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCommunity Aware Random Walk for Network Embedding
Social network analysis provides meaningful information about behavior of network members that can be used for diverse applications such as classification, link prediction. However, network analysis is computationally ex…
General ClassificationLink PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Progressive Representation Adaptation for Weakly Supervised Object Localization
We address the problem of weakly supervised object localization where only image-level annotations are available for training object detectors. Numerous methods have been proposed to tackle this problem through mining ob…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Deep Determinantal Point Process for Large-Scale Multi-Label Classification
We study large-scale multi-label classification (MLC) on two recently released datasets: Youtube-8M and Open Images that contain millions of data instances and thousands of classes. The unprecedented problem scale poses …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment
In the context of the Electronic Health Record, automated diagnosis coding of patient notes is a useful task, but a challenging one due to the large number of codes and the length of patient notes. We investigate four mo…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentence+1Convolutional Neural Networks (CNNs) for power system big data analysis
The concept of automated power system data analysis using Deep Neural Networks (as part of the routine tasks normally performed by Independent System Operators) is explored and developed in this paper. Specifically, we p…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSubset Labeled LDA for Large-Scale Multi-Label Classification
Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address multi-label learning tasks. Previous work has shown it to perform in par wit…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective
For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label classificat…
General ClassificationMatrix CompletionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Word Embeddings for Multi-label Document Classification
In this paper, we analyze and evaluate word embeddings for representation of longer texts in the multi-label classification scenario. The embeddings are used in three convolutional neural network topologies. The experime…
ClassificationDocument ClassificationGeneral ClassificationMulti-Label Classification+4Initializing neural networks for hierarchical multi-label text classification
Many tasks in the biomedical domain require the assignment of one or more predefined labels to input text, where the labels are a part of a hierarchical structure (such as a taxonomy). The conventional approach is to use…
ClassificationGeneral ClassificationMulti-Label ClassificationMulti Label Text Classification+4Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding. It is an important step in data analysis for funding and reimbursement, as well as hea…
Decision MakingDeep LearningFeature EngineeringGeneral Classification+2Detecting mentions of pain and acute confusion in Finnish clinical text
We study and compare two different approaches to the task of automatic assignment of predefined classes to clinical free-text narratives. In the first approach this is treated as a traditional mention-level named-entity …
General ClassificationInformation RetrievalMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Several real-world applications require real-time prediction on resource-scarce devices such as an Internet of Things (IoT) sensor. Such applications demand prediction models with small storage and computational com…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPrediction