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Multi-class Classification

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보존된 평가 기록: 5

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/uf-hobi-informatics-lab/ClinicalTransformerRelationExtraction

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Clinical Relation Extraction Using Transformer-based Models

https://github.com/varadhbhatnagar/Hostile-Post-Detection-in-Hindi

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Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi

https://github.com/vietnlp/vlsp2019_hatespeech_task

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HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social Good

https://github.com/viper-geneva/fchc-gnn-hierarchical

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Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction

https://github.com/wangqixun/mfpsg

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1st Place Solution for PSG competition with ECCV'22 SenseHuman Workshop

https://github.com/wbsg-uni-mannheim/wdcproducts

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WDC Products: A Multi-Dimensional Entity Matching Benchmark

https://github.com/wearepal/okapi

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Okapi: Generalising Better by Making Statistical Matches Match

https://github.com/wilsonmqz/clf

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Learning from Concealed Labels

https://github.com/wwangwitsel/scarce

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Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical

https://github.com/xuhwang/midx_journal

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Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and Applications

https://github.com/yasir-ali-farrukh/gnn4id

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XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model

https://github.com/yjlee22/kan-ecoc

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Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output Codes

https://github.com/ylincen/turs

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Truly Unordered Probabilistic Rule Sets for Multi-class Classification

https://github.com/yookyungkho/mav

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Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification

https://github.com/yromano/fair_dummies

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Achieving Equalized Odds by Resampling Sensitive Attributes

https://github.com/ythuang02/AppealCase

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AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios

https://github.com/yujinhan98/csforest

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Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection

https://github.com/yuxiaohuang/research

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CIGAN: A Python Package for Handling Class Imbalance using Generative Adversarial Networks

https://github.com/ywzcode/CS-adv

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Domain Adaptation with Cauchy-Schwarz Divergence

https://github.com/yxiangxiong/disgan

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Distance Guided Generative Adversarial Network for Explainable Binary Classifications

https://github.com/zalexis12/proximal-mean-field-learning

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Proximal Mean Field Learning in Shallow Neural Networks

https://github.com/zara77/t-cell-receptor-protein-sequences-and-sparse-coding

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T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification

https://github.com/zhangrenyuuchicago/balance

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Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing

https://github.com/zhangxiaoyu11/OmiVAE

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Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

https://github.com/zhiwenyou103/SciPrompt

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SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics

https://github.com/ziyiliang/conformalized_early_stopping

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Conformal inference is (almost) free for neural networks trained with early stopping

https://github.com/zoeyou/autotaxo

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Technological taxonomies for hypernym and hyponym retrieval in patent texts

https://gitlab.com/prasanthcakewalk/code-and-data-availability

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InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised Classification

https://zenodo.org/record/7608802

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The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification

https://github.com/ATTPC/attpc-event-classification

Machine Learning Methods for Track Classification in the AT-TPC

https://github.com/AmanDaVinci/Universal-Sentence-Representations

SentEval: An Evaluation Toolkit for Universal Sentence Representations

https://github.com/CarlsonZhuo/primal_dual_frank_wolfe

Primal-Dual Block Frank-Wolfe

https://github.com/Chanakya-Ekbote/GANs_and_VAC_for_RNA-Sequences

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

https://github.com/Chandan-IITI/LMKAD

Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification

https://github.com/Cogito2012/DEAR

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/Cris-Nguyen/Speech-Emotion-Recognition

Multimodal Speech Emotion Recognition and Ambiguity Resolution

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