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 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.
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.
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.
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.
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.
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.
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 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.
Conditional generators learn the data distribution for each class in a multi-class scenario and generate samples for a specific class given the right input from the latent space. In this work, a method known as "Versatile Auxiliary Classifier with Generative Adversarial Network" for multi-class scenarios is presented. …
Solves multi-class imbalanced data problem with geometry-based sampling and synthetic data.
problem Handling imbalanced multi-class data in classification problems.
method Two novel methods: undersampling and oversampling.
result Efficacy demonstrated through comparison with state-of-the-art methods.
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.
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.
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…
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…
Paper tackles noisy similarity labels for multi-class classification.
problem Learning multi-class classifiers from noisy similarity-labeled data.
method Proposes a method using a noise transition matrix to learn from noisy data.
result Demonstrates superior performance compared to state-of-the-art methods.
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.
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…
We introduce a new Bayesian multi-class support vector machine by formulating a pseudo-likelihood for a multi-class hinge loss in the form of a location-scale mixture of Gaussians. We derive a variational-inference-based training objective for gradient-based learning. Additionally, we employ an inducing point approxima…
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.
This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation. The proposed method, meta classification learning, optimizes a binary classifier for pairwise similarity prediction a…
Machine Learning has become very famous currently which assist in identifying the patterns from the raw data. Technological advancement has led to substantial improvement in Machine Learning which, thus helping to improve prediction. Current Machine Learning models are based on Classical Theory, which can be replaced b…
Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been…
Improves ROC/AUC for multi-class classification.
problem Lack of sensible plots, sensitivity to imbalanced data, inability to specify mis-classification cost, and lack of evaluation uncertainty quantification.
method Factorizes multi-class ROC into a one-dimensional vector representation for visualization and summary.
result Provides a binary AUC-equivalent summary and mis-classification weights specification.
Extends PCVM for multi-class classification with improved accuracy.
problem Lack of probabilistic outputs and contradictory predictions in multi-class classification.
method Proposes mPCVM with two learning algorithms: top-down and bottom-up.
result Superior performance, especially with many classes, validated on synthetic and benchmark data.
Unified framework for set-valued classification tackles ambiguous multi-class datasets.
problem Ambiguous multi-class datasets in modern statistics.
method Unified statistical framework encompassing various set-valued classification formulations.
result Infinite sample optimal strategies and plug-in principle for data-driven algorithms.
New algorithm boosts classification for imbalanced data.
problem Accurately classifying observations in severely imbalanced datasets.
method SAMME.C2 algorithm blending boosting and cost-sensitive techniques.
result Consistently superior performance in imbalanced classification problems.
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%.
Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been…
Learn class-invariant and symmetry-equivariant representations for multi-class data.
problem Deep neural networks learn opaque representations; we aim to make them more transparent.
method Probabilistic modelling with two separate latent variables: invariant and equivariant.
result Qualitative and quantitative performance competitive with other methods, with little tuning.
A new algorithm for faster model selection in twin multi-class SVM.
problem Challenges in effective solution of multi-classification and fast model selection in twin multi-class SVM.
method Sample data set partition strategy, Lagrangian multipliers, piecewise linear update, initialization algorithm, and event-based iteration.
result Comparable classification performance achieved without solving quadratic programming problems.
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.
Efficient multi-class classification with well-calibrated uncertainty.
problem Trade-off between uncertainty calibration and speed in multi-class Gaussian process classification.
method Proposes a new likelihood function leading to a conditionally conjugate model with efficient variational inference.
result Up to two orders faster than state-of-the-art methods with well-calibrated uncertainty estimates.
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.
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.
We show that the multi-class support vector machine (MSVM) proposed by Lee et. al. (2004), can be viewed as a MAP estimation procedure under an appropriate probabilistic interpretation of the classifier. We also show that this interpretation can be extended to a hierarchical Bayesian architecture and to a fully-Bayesia…
We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification cost…
Alternative method improves SVM for data classification.
problem SVM's weaknesses in time processing, optimization, multi-class generalization, and unbalanced classes.
method Minimum distance to optimal subspaces containing mapped classes.
result Improved performance with fewer shortcomings.
Generating user interpretable multi-class predictions in data rich environments with many classes and explanatory covariates is a daunting task. We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised topic model for multi-class classification that can handle both many classes as well as many co…
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…
Proposes BCOPS for balanced and outlier detection in multi-class classification.
problem Classification problems with different training and test distributions.
method BCOPS combines supervised learning with conformal prediction to optimize out-of-sample performance and detect outliers.
result BCOPS constructs prediction sets with finite-sample coverage guarantees and outlier detection rate estimation.
Study compares BERT and XLNet for multi-class categorization of product descriptions.
problem Robustness of multi-class categorization using pre-trained contextualized language models.
method Fine-tuning BERT and XLNet on Amazon product data for multi-class classification.
result Performance decreases linearly with the number of class labels, with BERT consistently outperforming XLNet.
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.
ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
problem Ambiguous outcome labels in real-world datasets hinder accurate multi-class classification.
method Information-theoretic classification accuracy (ITCA) and search strategies (greedy, breadth-first) guide label combination.
result ITCA improves prediction accuracy and identifies ambiguous labels across diverse applications.
Proposes a new method for multi-class classification with well-calibrated predictions.
problem Improving the accuracy and reliability of multi-class classification models.
method Trains data in a latent space induced by an (n−1)-dimensional simplex, then extends and fits a regression model. result Demonstrates a well-calibrated classifier with improved prediction and calibration properties.
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.