Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes
Background and Objective: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding proc…
BIG-bench Machine LearningDeep LearningMulti-Label ClassificationMulti-Label Classification Of Biomedical Texts+1Deep Learning with a Rethinking Structure for Multi-label Classification
Multi-label classification (MLC) is an important class of machine learning problems that come with a wide spectrum of applications, each demanding a possibly different evaluation criterion. When solving the MLC problems,…
ClassificationDeep LearningGeneral ClassificationMulti-Label Classification+1Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification
Recently the deep learning techniques have achieved success in multi-label classification due to its automatic representation learning ability and the end-to-end learning framework. Existing deep neural networks in multi…
ClassificationComputational EfficiencyEmotion ClassificationGeneral Classification+4Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network
Automated pavement crack detection is a challenging task that has been researched for decades due to the complicated pavement conditions in real world. In this paper, a supervised method based on deep learning is propose…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONStructured PredictionMulti-Label Learning from Medical Plain Text with Convolutional Residual Models
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR da…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Learning+3Applying an Ensemble Learning Method for Improving Multi-label Classification Performance
In recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algo…
ClassificationEnsemble LearningGeneral ClassificationMulti-Label Classification+1Generalized Graph Embedding Models
Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs. Hence, learning from and with graph embeddings has drawn a great deal of rese…
General ClassificationGraph EmbeddingKnowledge Base CompletionMulti-Label Classification+1Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise
The problem of multi-speaker localization is formulated as a multi-class multi-label classification problem, which is solved using a convolutional neural network (CNN) based source localization method. Utilizing the comm…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting
One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings --- e.g., a big shopping mall and a university campus --- is a scalable indoor localization technique…
General ClassificationIndoor LocalizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMining Supervisor Evaluation and Peer Feedback in Performance Appraisals
Performance appraisal (PA) is an important HR process to periodically measure and evaluate every employee's performance vis-a-vis the goals established by the organization. A PA process involves purposeful multi-step mul…
ClusteringGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Intelligent EHRs: Predicting Procedure Codes From Diagnosis Codes
In order to submit a claim to insurance companies, a doctor needs to code a patient encounter with both the diagnosis (ICDs) and procedures performed (CPTs) in an Electronic Health Record (EHR). Identifying and applying …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model
The analysis of customer feedback is useful to provide good customer service. There are a lot of online customer feedback are produced. Manual classification is impractical because the high volume of data. Therefore, the…
General ClassificationMulti-Label ClassificationSarcasm DetectionSentence+3ADAPT at IJCNLP-2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task
In this age of the digital economy, promoting organisations attempt their best to engage the customers in the feedback provisioning process. With the assistance of customer insights, an organisation can develop a better …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification
Multi-label classification is the task of predicting a set of labels for a given input instance. Classifier chains are a state-of-the-art method for tackling such problems, which essentially converts this problem into a …
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPredictionMulti-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize …
General ClassificationKnowledge GraphsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification
We study multi-label classification (MLC) with three important real-world issues: online updating, label space dimensional reduction (LSDR), and cost-sensitivity. Current MLC algorithms have not been designed to address …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Multi-label Dataless Text Classification with Topic Modeling
Manually labeling documents is tedious and expensive, but it is essential for training a traditional text classifier. In recent years, a few dataless text classification techniques have been proposed to address this prob…
ClassificationGeneral ClassificationGPUMulti-Label Classification+3Vertex-Context Sampling for Weighted Network Embedding
In recent years, network embedding methods have garnered increasing attention because of their effectiveness in various information retrieval tasks. The goal is to learn low-dimensional representations of vertexes in an …
Information RetrievalMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNetwork Embedding+2Natural Language Informs the Interpretation of Iconic Gestures: A Computational Approach
When giving descriptions, speakers often signify object shape or size with hand gestures. Such so-called {`}iconic{'} gestures represent their meaning through their relevance to referents in the verbal content, rather th…
FormMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPrototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFB…
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION