Papers Ordinal Classification
“Ordinal Classification” 태그가 달린 논문 92편 · 필터 해제
YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets
We implemented the sentiment system in all five subtasks for English and Spanish. All subtasks involve emotion or sentiment intensity prediction (regression and ordinal classification) and emotions determining (multi-lab…
ClassificationDomain AdaptationEnsemble LearningGeneral Classification+4SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection
The system developed by the SSN MLRG1 team for Semeval-2018 task 1 on affect in tweets uses rule based feature selection and one-hot encoding to generate the input feature vector. Multilayer Perceptron was used to build …
Classificationfeature selectionGeneral ClassificationOrdinal Classification+2UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models
Our submission to the SemEval-2018 Task1: Affect in Tweets shared task competition is a supervised learning model relying on standard lexicon features coupled with word embedding features. We used an ensemble of diverse …
General ClassificationOrdinal ClassificationSentiment AnalysisImage Ordinal Classification and Understanding: Grid Dropout with Masking Label
Image ordinal classification refers to predicting a discrete target value which carries ordering correlation among image categories. The limited size of labeled ordinal data renders modern deep learning approaches easy t…
Age EstimationClassificationData AugmentationGeneral Classification+1SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets
The paper describes the best performing system for the SemEval-2018 Affect in Tweets (English) sub-tasks. The system focuses on the ordinal classification and regression sub-tasks for valence and emotion. For ordinal cla…
Domain AdaptationGeneral ClassificationOrdinal ClassificationPosition+1Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification
This paper describes the participation of Amobee in the shared sentiment analysis task at SemEval 2018. We participated in all the English sub-tasks and the Spanish valence tasks. Our system consists of three parts: trai…
General ClassificationOrdinal ClassificationSentiment AnalysisSentiment Classification+1EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets
This paper describes our system that has been used in Task1 Affect in Tweets. We combine two different approaches. The first one called N-Stream ConvNets, which is a deep learning approach where the second one is XGboost…
Emotion RecognitionGeneral ClassificationOrdinal ClassificationregressionUnimodal probability distributions for deep ordinal classification
Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propose a straightforward technique to constrain discrete ordinal probability distribu…
ClassificationGeneral ClassificationOrdinal ClassificationA simple squared-error reformulation for ordinal classification
In this paper, we explore ordinal classification (in the context of deep neural networks) through a simple modification of the squared error loss which not only allows it to not only be sensitive to class ordering, but a…
ClassificationGeneral ClassificationOrdinal ClassificationElectre Tri-Machine Learning Approach to the Record Linkage Problem
In this short paper, the Electre Tri-Machine Learning Method, generally used to solve ordinal classification problems, is proposed for solving the Record Linkage problem. Preliminary experimental results show that, using…
BIG-bench Machine LearningGeneral ClassificationOrdinal ClassificationNoncrossing Ordinal Classification
Ordinal data are often seen in real applications. Regular multicategory classification methods are not designed for this data type and a more proper treatment is needed. We consider a framework of ordinal classification …
ClassificationGeneral ClassificationOrdinal ClassificationoAdaBoost: An AdaBoost Variant for Ordinal Classification
Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a…
ClassificationGeneral ClassificationInformation RetrievalMulti-Label Classification+2