Unified binary and multiclass margin-based classification methods.
problem No consensus on multiclass loss functions analogous to binary margin loss.
method Showed multiclass loss functions can be expressed in relative margin form.
result Extended classification-calibration result to multiclass.
Paper studies multiclass classifiers from binary classifiers, proving methods and demonstrating advantages.
problem Constructing efficient multiclass classifiers from binary ones.
method Two methods: one vs. all and hierarchical classification, with a new leverage-hierarchical method introduced.
result Proves upper bounds and exact formulas for multiclass regret in terms of binary regrets.
Proposes top-label calibration and M2B framework for multiclass to binary calibration.
problem Multiclass calibration and interpretation issues.
method Top-label calibration and M2B reduction framework.
result M2B + HB achieves lower calibration error than other methods.
The paper analyzes how overparameterized models can generalize well in multiclass classification.
problem Generalization in multiclass classification with overparameterized models.
method Survival/contamination analysis framework adapted for multiclass classification.
result Multiclass classification can generalize well even with many classes, unlike regression tasks.
Nested dichotomies for multiclass tasks often fail to calibrate probabilities.
problem Poor probability calibration in nested dichotomies for multiclass classification.
method Transforming multiclass problems into binary ones using a tree structure, and applying various calibration strategies.
result Improving accuracy and log-loss by calibrating both internal base models and the nested dichotomy structure.
Binary and multiclass epilepsy detection methods using EEG features.
problem Epilepsy diagnosis from EEG data.
method Feature extraction from power spectrum, spectrogram, and bispectrogram; eight machine learning algorithms used.
result Random forest and backpropagation algorithms achieved highest accuracy for binary and multiclass classification.
This work extends implicit bias analysis to multiclass classification using a new loss framework.
problem The implicit bias of gradient descent on multiclass data without explicit regularization.
method Employing the PERM framework to introduce a multiclass extension of the exponential tail property.
result Extended implicit bias result to multiclass classification using a new loss framework.
New method debiases word embeddings for multiclass settings like race and religion.
problem Word embeddings in online texts perpetuate human stereotypes, including race and religion.
method Proposes a novel methodology to debias word embeddings in multiclass settings.
result Demonstrates robust multiclass debiasing that maintains NLP task efficacy.
A new method combines simple binary classifiers to build complex multiclass classifiers, achieving performance limits in a Gaussian setting.
problem Building a sophisticated multiclass classifier from simple binary decisions.
method Combining O(logK) simple binary classifiers to form a K-class classifier. result Explicit performance bounds across various decoding and dimensional regimes for a stylized Gaussian setting.
New binary approach for multiclass classification scales logarithmically with classes.
problem Efficient multiclass classification for large number of classes.
method Proves a boosting theorem and translates it into an algorithm.
result Exponential speed improvements for large number of classes.
New binary classification techniques help multiclass classification by aggregating proper learners.
problem Multiclass classification faces a properness barrier that prevents optimal learning by proper learners.
method Aggregations of proper binary learners, generalized to multiclass settings, achieve optimal sample complexity.
result Optimal binary learners can achieve sample complexity $O\left(\frac{d_G + \ln(1 / δ)}ε
ight)$ for classes with finite Graph dimension dG. Study extends binary omniprediction to multiclass setting with improved sample complexity.
problem Suboptimality bounds for each loss function against infinite comparator family in multiclass prediction.
method Design of a framework for solving Blackwell approachability problems with coupled actions.
result Sample complexity of ≈ε−(k+1) for ε-omniprediction in a k-class problem. A new weighted MCC measure improves classifier performance evaluation.
problem Lack of measures sensitive to observation weights in multiclass classification.
method Proposes weighted versions of Pearson-Matthews Correlation Coefficient (MCC) for binary and multiclass classification.
result Weighted MCC values are higher for classifiers that perform better on highly weighted observations.
API technique improves multiclass GAM interpretability.
problem Interpretability breakdown in multiclass GAMs.
method Developed two axioms and API technique for multiclass GAMs.
result API technique transforms multiclass GAMs to be visually interpretable.
Investigates multiclass classification with rejection, achieving state-of-the-art performance and deriving calibration conditions.
problem Multiclass classification with rejection, where a classifier can choose not to predict.
method Two approaches: simultaneous training of classifier and rejector, and confidence scores with rejection criteria.
result Calibration is hard for general loss functions in multiclass case, but achievable with specific rejection criteria.
Novel multiclass SVM framework classifies lithology from well logs.
problem Classifying lithology types from well logs.
method Multiclass SVM approach using one-against-all strategy.
result Multiclass SVM outperforms other classifiers in classification accuracy.
Extends boosting to online multiclass problems with optimal algorithms.
problem Online multiclass classification challenges.
method Defines and justifies a weak learning condition for online multiclass boosting. Proposes an adaptive algorithm.
result Optimal boosting algorithm for online multiclass problems.
Reduces multiclass and regression compression schemes to binary ones.
problem Developing efficient learning algorithms for multiclass and regression problems.
method Reduces sample compression schemes for binary classes to multiclass and regression settings.
result Establishes new compression schemes for multiclass and regression problems.
Proposes a new method to improve multiclass probability calibration.
problem Uncalibrated class probabilities in multiclass classifiers leading to over-confidence.
method Dirichlet calibration method applicable to any model class, derived from Dirichlet distributions.
result Improved probabilistic predictions across various datasets and classifiers.
Logistic regression outperforms other models in radiology report classification.
problem Efficiently labeling radiology reports for model training.
method Simple machine learning models, including logistic regression, for binary and multiclass classification.
result Logistic regression binary classifier achieves above 0.9 average precision in unseen reports.
Study shows exponential error reduction in multiclass classification without bias-variance trade-off.
problem Multiclass classification with margin conditions.
method Analysis of classification error under hard-margin conditions.
result Exponential decrease in classification error without bias-variance trade-off.
New bounds show multiclass problems reduce overfitting from test set reuse.
problem Overfitting from test set reuse in multiclass problems.
method Upper and lower bounds on bias of attacks, practical and inefficient attacks.
result Multiclass problems mitigate overfitting from test set reuse.
We prove new fast learning rates for the one-vs-all multiclass plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging drifting distribution. These are two typical scenarios where training data are not iid. The learning rates are obtained under a multiclass vers…
New research determines the optimal sample complexity for multiclass and list learning.
problem Determining the optimal sample complexity for multiclass classification.
method Algebraic characterization of multiclass hypothesis classes in terms of their DS dimension.
result Proves a longstanding conjecture and determines the optimal dependence of sample complexity on DS dimension.
This thesis formalizes metric selection for machine learning applications.
problem Selecting the right performance metric for machine learning models.
method Metric Elicitation framework using pairwise preference feedback.
result Novel strategies for eliciting various classification metrics robust to noise.
The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this work to more complex outputs, by providing generalizations of the C-bound to the multiclass and multi-label settings.
Develops NPMC method for noisy labels, improving multiclass classification accuracy.
problem Asymmetric misclassification costs and label noise in multiclass classification.
method Empirical likelihood approach using exponential tilting density ratio model.
result Root n consistent and asymptotically normal estimators for clean labels and noise mechanism.
New method for multiclass classification reduces error bounds.
problem High-dimensional multiclass classification with sparse coefficients.
method Sparse multinomial logistic regression with convex penalties.
result Plug-in classifiers achieve minimax generalization error bounds.
Extends quantum learning theory to multiclass and online settings.
problem Quantum learning theory for batch and online learning.
method Adapts classical models to quantum settings, introduces new online learning model.
result Quantum and classical sample complexities have the same form for various learning scenarios.
Extends boosting to multiclass online agnostic classification.
problem Online multiclass classification with weak learners.
method Reduces multiclass online agnostic boosting to online convex optimization.
result First boosting algorithm for online agnostic multiclass classification.
New bounds show agnostic multiclass learning depends on two dimensions: Natarajan and Daniely-Shalev-Shwartz.
problem Understanding sample complexity in multiclass classification with agnostic learning.
method Developed a novel online procedure based on a self-adaptive multiplicative-weights algorithm.
result Agnostic sample complexity bounds are in the form of DS^(1.5)/ε + Nat/ε^2, nearly tight up to a √DS factor.
Unified approach to multiclass classification using Gabriel graphs.
problem Improving multiclass classification accuracy and efficiency.
method Integrates Gabriel graphs for binary and multiclass classification, proposing new activation functions and support edge neurons.
result Experimental results show superior performance compared to previous GG-based classifiers.
Survey on assessing and improving classifier calibration for better decision making.
problem Ensuring classifiers correctly quantify prediction uncertainty.
method Overview of principles, methods, and evaluation metrics for calibration.
result New methods and extensions from binary to multiclass settings.
Random projections have been applied in many machine learning algorithms. However, whether margin is preserved after random projection is non-trivial and not well studied. In this paper we analyse margin distortion after random projection, and give the conditions of margin preservation for binary classification problem…
A new method combines bagging and threshold-moving for imbalanced data.
problem Class imbalance in data mining hinders model performance.
method Probability thresholding bagging (PT-bagging) that preserves natural class distribution.
result PT-bagging improves model performance on imbalanced data.
New methods detect unfairness in multiclass classifiers using DCP.
problem Detecting unfairness in multiclass classifiers.
method Generalizes DCP to multiclass, provides optimization methods.
result Detects classifiers treating a significant fraction of the population unfairly.
New reformulations for multiclass classification problems using optimal transport.
problem Adversarial multiclass classification problems.
method Multimarginal optimal transport formulation.
result Reveals geometric structure and extends binary classification results.
A new meta-learning method for faster adaptive multiclass classification.
problem Efficiently designing multiclass classifiers that adaptively merge classes into a hierarchical structure.
method Adaptive hierarchical classification tree construction using empirical Bayes error rates and minimal spanning trees.
result The proposed method can learn faster and achieve competitive accuracy compared to existing methods.
Learnable multiclass hypothesis classes don't always have a sample compression scheme of fixed size.
problem The limitation of sample compression schemes for multiclass hypothesis classes.
method Analysis of DS dimension and sample compression schemes.
result Learnable multiclass hypothesis classes do not always have a sample compression scheme of fixed size.
New algorithms reduce overfitting in multiclass classification.
problem Excessive reuse of test datasets in machine learning leads to overfitting, especially in multiclass classification.
method Developed computationally efficient algorithms to reduce overfitting bias in multi-class classification.
result Achieved overfitting bias of Θ(√(k/(mn)), k/n), matching known upper bounds.
Study proves existence of robust classifiers in multiclass adversarial training.
problem Proves existence of robust classifiers in multiclass adversarial training.
method Three models of adversarial training in multiclass classification, proving existence of Borel measurable robust classifiers.
result Proves existence of Borel measurable robust classifiers in each model.
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and ge…
Online boosting for multiclass classification with limited feedback.
problem Online multiclass classification with bandit feedback.
method Proposed unbiased loss estimate and extended full information boosting algorithms to bandit setting.
result Asymptotic error bounds match full information counterparts, with larger sample complexity due to limited feedback.
Proposes a new method for likelihood-to-evidence ratio estimation.
problem Inherent bias in multiclass ratio estimation framework.
method Contrastive Neural Ratio Estimation (C-NeR) framework.
result C-NeR framework removes bias and improves diagnostic reliability.
The support vector machine (SVM) was originally designed for binary classifications. A lot of effort has been put to generalize the binary SVM to multiclass SVM (MSVM) which are more complex problems. Initially, MSVMs were solved by considering their dual formulations which are quadratic programs and can be solved by s…
We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective. In particular, we propose a geometric regularization technique to find the submanifold corresponding to a robust estimator of the class probability P(y∣x). The regularization term meas…
This work characterizes when a hypothesis class can be k-list learned.
problem Characterizing when a hypothesis class can be k-list learned.
method Introducing the k-DS dimension and proving the equivalence of k-list learnability and the finiteness of the k-DS dimension.
result A hypothesis class is k-list learnable if and only if the k-DS dimension is finite.
We study consistency properties of surrogate loss functions for general multiclass learning problems, defined by a general multiclass loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning pro…