A new method for generating samples in multi-class scenarios using GANs and classifiers.
problem Generating samples for specific classes in multi-class scenarios.
method Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN) method where the generator is conditional and the classification error is backpropagated.
result The method improves sample generation for specific classes in multi-class scenarios.
DCSVM efficiently classifies multi-class data using SVMs with smart partitioning.
problem Multi-class classification using SVMs with high computational cost.
method Divide and conquer approach with smart partitioning of data.
result Reduces the number of classes in each step, making final decisions in logarithmic or linear steps.
A new probabilistic model for yes/no crowdsourcing in multi-class classification.
problem Difficulty in discerning among all possible classes in full questions.
method Developed a full probabilistic model for shorter yes/no queries, estimating joint posterior distributions and using Monte Carlo Sampling and Black Box Variational Inference.
result Comparable results to full query crowdsourcing, highlighting the importance of modeling labelers' failures.
Paper proposes an approximate margin method for fast multi-class classification.
problem Challenges in multi-class classification with many classes.
method Uses ANN search structures and LSH for approximate margin estimation.
result Approximate margin method is highly competitive in time, memory, and performance.
OPLTs online train label trees for multi-label and multi-class classification.
problem Online multi-label and multi-class classification challenges.
method Fully online training of label trees without prior knowledge.
result Strong theoretical guarantees and low complexity.
Recent studies in the literature have paid much attention to the sparsity in linear classification tasks. One motivation of imposing sparsity assumption on the linear discriminant direction is to rule out the noninformative features, making hardly contribution to the classification problem. Most of those work were focu…
New bounds on majority voting's accuracy for multi-class classification problems.
problem Determining the accuracy of majority voting for multi-class classification.
method Analyzing the majority voting function under different voter conditions and distributions.
result The error rate of majority voting exponentially decays or grows with the number of voters under certain conditions.
This thesis tackles learning with multi-class abstention and multi-expert deferral, improving model reliability and efficiency.
problem Improving model reliability and efficiency in large language models (LLMs) by leveraging multiple experts.
method Developed new surrogate losses and consistency guarantees for multi-class classification and regression with deferral.
result Strong consistency guarantees for surrogate losses in multi-class classification and regression with deferral.
Paper introduces new loss functions for multi-class abstention learning.
problem Learning with multi-class classification and the ability to abstain.
method Developed new families of surrogate losses for abstention.
result Proved strong consistency guarantees for new surrogate losses.
This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large. These robust and parameter free algorithms are essentially iterative least-squares updates and very versatile both in theory and in practice. O…
Enhanced H-consistency bounds derived under relaxed conditions.
problem Quantifying the relationship between zero-one estimation error and surrogate loss estimation error.
method Relaxing the condition on the surrogate loss conditional regret and presenting a general framework for establishing enhanced H-consistency bounds. result Derivation of more favorable H-consistency bounds in various scenarios. The paper develops GP classifiers for noisy inputs in multi-class classification.
problem Input noise in real-world data affects supervised learning performance.
method Developed multi-class Gaussian Process classifiers using variational inference.
result The proposed methods outperform noise-ignoring GP classifiers in predictive distribution.
Enhances projection pursuit tree classifier with visual diagnostics for better multi-class classification.
problem Rigidity of original algorithm limits performance in complex high-dimensional classification problems.
method Allowing more splits and flexible class groupings in projection pursuit computation, and developing visual diagnostics.
result Demonstrates enhanced classifier performs as intended through interactive visual diagnostics.
Improved analysis for extreme multi-class CRL with better sample complexity.
problem Theoretical sample complexity of CRL in extreme multi-class settings is poorly understood.
method Improved U-Statistics estimator to capture class concentration, proving O(k) sample complexity. result Sample complexity is O(k) for extreme multi-class learning, independent of class distribution. LIMEtree offers faithful explanations for multiple classes in predictive models.
problem Generating explanations for several classes can be difficult due to conflicting evidence.
method LIMEtree uses multi-output regression trees for consistent and faithful explanations of multiple classes.
result LIMEtree provides diverse explanation types and outperforms LIME in evaluations.
A sequential classifier minimizes test samples for binary and multi-class classification.
problem Minimizing test samples for sequential classification with unknown distributions.
method Proposes a classifier for binary and multi-class problems, analyzing error probabilities and extending results.
result Significant advantage over non-sequential classifiers, achieving same exponents without rejection option.
This research sets limits on how complex multi-class learning problems can be.
problem Understanding the complexity of multi-class classification problems.
method Established upper bounds on Natarajan dimensions for specific function classes.
result Upper bounds on Natarajan dimensions for multi-class decision trees, random forests, and neural networks.
Study proposes a method for identifying important variables in multi-class classification problems.
problem Lack of studies on variable selection in nonparametric classification models, especially for multi-class problems.
method Sparse non-parametric density estimation approach for identifying high impacts variables.
result Proposed method identifies important variables for each class in multi-class classification problems.
No multi-class labels needed for multi-class classification.
problem Multi-class classification without requiring class-specific labels.
method Meta classification learning using pairwise similarity prediction.
result The method learns a multi-class classifier from binary classifier for pairwise similarity.
Paper develops new algorithms for unsupervised multi-class domain adaptation.
problem Improving performance of machine learning models across different domains without labeled data.
method Introduces MCSD divergence and a new domain adaptation bound, develops adversarial learning objectives, and proposes new algorithms like McDalNets and SymmNets.
result New algorithms improve performance across different domain adaptation settings.
CSI detects novelty by contrasting shifted instances, outperforming existing methods.
problem Detecting samples from outside the training distribution.
method Contrastive learning with distributionally shifted augmentations.
result CSI outperforms existing methods in various novelty detection scenarios.
Develops algorithms for multi-class Neyman-Pearson classification with cost sensitivity.
problem Asymmetric misclassification costs in multi-class classification problems.
method Establishes connection with cost-sensitive learning, proposes two algorithms, extends NP oracle properties.
result Proposes algorithms with theoretical guarantees for multi-class Neyman-Pearson classification.
This paper reviews metrics for evaluating multi-class classification models.
problem Evaluating and comparing multi-class classification models.
method Review and analysis of metrics.
result Promising multi-class metrics are highlighted and their usages are demonstrated.
Study on H-consistency bounds for machine learning surrogates.
problem Estimating target loss error relative to surrogate loss error in machine learning.
method Developed H-consistency bounds for various surrogates and loss functions. result Stronger guarantees than existing methods, offering distribution-dependent and -independent bounds.
We consider a problem of risk estimation for large-margin multi-class classifiers. We propose a novel risk bound for the multi-class classification problem. The bound involves the marginal distribution of the classifier and the Rademacher complexity of the hypothesis class. We prove that our bound is tight in the numbe…
Paper proposes GEG to enhance fairness in binary and multi-class classification.
problem Fairness in multi-class classification tasks is under-explored.
method Formulates multi-objective problem between effectiveness and fairness constraints, proposes GEG algorithm.
result GEG improves fairness up to 92% and decreases accuracy up to 14%.
Study improves multi-class domain generalization with a new error bound.
problem Improving multi-class classification performance across multiple domains.
method Kernel-based learning algorithm with a logarithmic generalization error bound.
result Achieved significant performance gains over a pooling strategy empirically.
Combines neural networks and STL for multi-class time-series classification.
problem Lack of interpretability in neural networks for time-series data.
method Proposes a method that uses neural networks to classify time-series data using STL specifications, introducing margin for multi-class classification and STL-based attributes for interpretability.
result Evaluations show improved interpretability and performance compared to state-of-the-art baselines.
A new method identifies class-specific covariates in multi-class prediction tasks.
problem Identifying covariates specifically associated with one or more outcome classes in multi-class prediction tasks.
method Introducing multi forests (MuFs) with multi-way and binary splits to measure class-associated discriminatory ability.
result The multi-class VIM specifically ranks class-associated covariates highly, unlike conventional VIMs.
Develops a multi-class classifier using quantum detection theory.
problem Improving multi-class classification models in machine learning.
method Inspired by quantum detection theory, develops a multi-class classifier.
result Demonstrates improved effectiveness of multi-class classification models.
Study identifies COVID-19 pneumonia from chest X-rays.
problem Identifying COVID-19 pneumonia from other types and healthy lungs using CXR images.
method Proposed a multi-class and hierarchical classification schema using CXR images, texture descriptors, and a pre-trained CNN model. Employed resampling algorithms and early/late fusion techniques.
result Achieved macro-avg F1-Score of 0.65 and F1-Score of 0.89 for COVID-19 identification in hierarchical classification scenario.
A new algorithm reduces imbalanced data classification errors in multi-class settings.
problem Imbalanced data classification, especially with noise and overlapping classes.
method MC-CCR algorithm combining cleaning and resampling.
result High robustness to noise and superior performance compared to state-of-the-art methods.
Symmetrizes loss functions to improve neural network robustness against noisy labels.
problem Designing robust loss functions for noisy labels in neural networks.
method Symmetrization of multi-class loss functions, focusing on cross-entropy and unhinged loss.
result The multi-class unhinged loss is the unique convex symmetric loss under suitable assumptions.
Paper proposes MMVFL for multi-class VFL with multiple participants.
problem Privacy-preserving multi-class VFL with multiple participants.
method Extends multi-view learning to enable label sharing among multiple VFL participants.
result MMVFL effectively shares label information among multiple VFL participants and matches multi-class classification performance.
Single neurons can perform as well as dense networks in binary and multi-class recognition tasks.
problem Designing efficient neural networks for recognition tasks.
method Investigated the use of single or multiple neurons in neural networks for binary and multi-class recognition tasks.
result Sparse networks can be as efficient as dense networks in both binary and multi-class tasks.
New calibration measures for multi-class classification improve model accuracy.
problem Calibration of multi-class classification models is insufficient for safety-critical applications.
method Developed new calibration measures and estimators for multi-class classification.
result Proposed estimators improve interpretability and accuracy of calibration measures.
Due to myriads of classes, designing accurate and efficient classifiers becomes very challenging for multi-class classification. Recent research has shown that class structure learning can greatly facilitate multi-class learning. In this paper, we propose a novel method to learn the class structure for multi-class clas…
Deep learning identifies smartphone users from motion sensor data.
problem Smartphone user identification using motion sensor signals.
method Transformed motion signals into images, trained CNN for classification, used SVM for few-shot identification.
result CNN achieved 89.75% multi-class user classification and 96.72% few-shot user identification accuracy.
We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples. The aim o…
We study consistency of learning algorithms for a multi-class performance metric that is a non-decomposable function of the confusion matrix of a classifier and cannot be expressed as a sum of losses on individual data points; examples of such performance metrics include the macro F-measure popular in information retri…
New scalable multi-class SVM for structured and unstructured data.
problem Classification of structured and unstructured data.
method Bayesian multi-class support vector machine with pseudo-likelihood, variational inference, and inducing point approximation.
result Outperforms competitor methods in training time and accuracy.
Paper tackles cybersecurity attack detection with an ensemble approach.
problem Challenges in multi-class classification for cyber security breaches.
method Designing a multi-node multi-class classification ensemble approach.
result Proposed approach outperforms full-data approach in multi-node data-censoring cases.
This paper analyzes dynamic ensemble selection and preprocessing for multi-class imbalanced datasets.
problem Class imbalance in multi-class datasets where majority classes have more instances.
method Examined dynamic selection techniques and data preprocessing methods for multi-class imbalanced problems.
result Dynamic ensemble improves AUC and G-mean compared to static ensemble.
New method calibrates multi-class predictions efficiently without sacrificing accuracy.
problem Efficiently calibrating multi-class predictions without sacrificing accuracy.
method Formulated robust projected smooth calibration and new recalibration algorithms.
result Achieves strong guarantees for binary classification tasks with polynomial complexity.
New method learns multi-class from single-class data with confidences.
problem Learning multi-class from single-class data without additional data.
method Empirical risk minimization framework for multi-class classification.
result Method achieves Bayes-consistency with noisy confidences.
Improves probability estimates for small datasets in multi-class problems.
problem Inaccurate probability estimates in classification tasks, especially on small datasets.
method Introduced Data Generation and Grouping algorithm to improve calibration on small datasets, then applied to multi-class problems.
result Calibration error can be decreased using the proposed approach.
The study analyzes multi-class teacher-student perceptron performance and generalization errors.
problem Analyzing multi-class classification with the teacher-student perceptron.
method Deriving asymptotic expressions for Bayes-optimal and empirical risk minimization (ERM) generalization errors.
result Regularised cross-entropy minimization yields close-to-optimal accuracy for multi-class classification.
Develops risk-averse fair multi-class classification methods.
problem Noisy, scarce, unreliable data in multi-class classification problems.
method Systemic risk models and risk-averse regularized decomposition method.
result Enforces fairness and improves performance with unreliable data.