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.
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.
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…
The study analyzes performance indices for class-imbalanced data and identifies conditions they must meet.
problem Distortions in performance indices under class imbalance.
method Identified two conditions for performance indices and analyzed four binary and five multi-class indices.
result Recommended appropriate indices for evaluating classifiers in class-imbalanced scenarios.
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.
New framework for learning from imbalanced data with theoretical guarantees.
problem Class imbalance in machine learning, especially in multi-class problems.
method Theoretical framework and new margin loss function for imbalanced classification.
result Proves strong H-consistency of the proposed margin loss function. In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very lim…
Improved risk assessment for UBI using telematics data and AdaBoost.
problem Class imbalance in predicting claims frequency for UBI.
method Cost-sensitive multi-class AdaBoost (SAMME.C2) algorithm.
result SAMME.C2 outperforms other models in handling class imbalances.
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.
Paper explains learning property of logistic and softmax losses for balanced and imbalanced class data.
problem Understanding and optimizing loss functions for deep neural networks with class imbalances.
method Analyzing necessary conditions for convergence of logistic and softmax losses in CNNs.
result Proposes a novel reweighted logistic loss function that improves performance over softmax loss.
SIGTRON improves classification accuracy for imbalanced datasets.
problem Improving classification accuracy for imbalanced datasets.
method SIGTRON is a new sigmoid function with a convex loss function for imbalanced classification.
result SIGTRON models outperform existing methods in balanced and imbalanced datasets.
A new method improves fault diagnostics and prognostics for class-imbalanced data.
problem Class imbalance in industrial fault diagnostics and prognostics.
method EWMOTE: EM-based Weighted Minority Oversampling TEchnique.
result EWMOTE achieves better performance on binary and multi-class imbalance learning tasks.
This paper introduces new loss functions for balanced multi-class classification.
problem Balancing class imbalance in multi-class classification.
method Introduces two new surrogate loss families: GLA and GCA.
result GCA losses offer stronger theoretical guarantees in imbalanced settings.
O-MedAL optimizes medical image analysis with online active deep learning.
problem Improving accuracy in medical image analysis with limited labeled data.
method Online Active Deep Learning method that queries examples maximizing average distance to training set.
result Significant performance improvements, including 6.30% accuracy boost with 25% labeled data.
Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. Howev…
PTBCC improves accuracy in multi-class annotation aggregation by learning from prototype confusion matrices.
problem Inaccurate and insufficient confusion matrices for annotators in multi-class classification tasks.
method PTBCC (ProtoType learning-driven Bayesian Classifier Combination) uses prototype confusion matrices to capture annotator expertise.
result PTBCC achieves up to 15% accuracy improvement and 3% higher average accuracy compared to existing methods.
Improved security of smart contracts by classifying them into four categories.
problem Detecting and classifying vulnerabilities in smart contracts efficiently.
method Used AWD-LSTM for multi-class classification, addressing class imbalance.
result Achieved a weighted average Fbeta score of 90.0%.
A novel 'outside the box' solution improves classification quality for imbalanced data.
problem Class imbalance in real-world data sets affects classifier performance.
method Enrichment technique using external data sets, implemented in three approaches.
result Average improvement of 27%, best case 66% in classification quality.
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.
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.
A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity an…
Robust cancer screening model using pre-trained ensembles for biomarkers.
problem Detecting early-stage cancer, especially in hard-to-diagnose cases like pancreatic cancer.
method Meta-trained Hyperfast model for robust classification, combined with ensembling of XGBoost and LightGBM.
result Achieved highest AUC of 0.9929 and robust performance on imbalanced datasets.
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.
Deep learning shows ETF imbalances are more informative than market imbalances.
problem Determining causality between ETF and market imbalances.
method Deep learning econometric methodology applied to stock and ETF transactions.
result ETF imbalance messages are more informative than market imbalance messages.
Improved stock price prediction model using generalized order flow imbalance.
problem Improving stock price prediction models using new order flow imbalance indicators.
method Proposed a generalized order flow imbalance construction method and applied it to CSI 500 stocks.
result Generalized Stationarized Order Flow Imbalance (log-GOFI) shows significant improvement in explaining stock price changes.
New index measures class imbalance impact on classification performance.
problem Class imbalance affects classifier performance beyond imbalance ratio.
method Theoretical study of Bayes optimal classifier, proposing IBI3 and BI3 measures. result Demonstrates the extent of imbalance impact on classification performance.
ART adapts class-wise resampling to improve imbalanced classification performance.
problem Class imbalance in classification tasks limits model performance.
method ART uses adaptive resampling based on class-wise performance metrics.
result ART consistently outperforms other methods on diverse benchmarks.
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.
This paper examines biases in foundation models under long-tailed data and proposes a method to mitigate parameter imbalance.
problem The bias introduced by imbalanced training data in foundation models affects long-tailed downstream tasks.
method The paper examines parameter imbalance and data imbalance, proposing a backdoor adjustment method to mitigate parameter imbalance.
result An average performance increase of about 1.67% on each dataset.
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.
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%.
Queue-based resampling tackles online class imbalance learning with selective resampling of past examples.
problem Online class imbalance learning under class imbalance and concept drift.
method Queue-based resampling algorithm that selectively includes past examples in the training set.
result Queue-based resampling outperforms state-of-the-art methods in terms of learning speed and quality.
We investigate the probability distribution of order imbalance calculated from the order flow data of 43 Chinese stocks traded on the Shenzhen Stock Exchange. Two definitions of order imbalance are considered based on the order number and the order size. We find that the order imbalance distributions of individual stoc…
This chapter tackles class imbalance in datasets to promote data democracy.
problem Class imbalance in datasets leading to biased decisions and policies.
method Statistical measures and data-level methods (oversampling, undersampling, etc.) applied to a real dataset.
result Popular data-level methods improve performance in handling class imbalance.
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.
We analyze anomaly detection class imbalance using a solvable model.
problem Class imbalance hampers anomaly detection performance.
method We use an exact solution of the teacher-student perceptron model through replica theory.
result Optimal train imbalance is often different from 50%, influenced by intrinsic imbalance and data abundance.
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.
Theoretical and empirical taxonomy of imbalance in binary classification.
problem Class imbalance degrades binary classification performance.
method Proposed a principled framework based on three scales: imbalance coefficient, sample-dimension ratio, and intrinsic separability. Derived closed-form Bayes errors and analyzed degradation across models.
result The triplet (η, κ, Δ) provides a model-agnostic explanation of imbalance-induced deterioration.
We propose a novel online learning algorithm for Restricted Boltzmann Machines (RBM), namely, the Online Generative Discriminative Restricted Boltzmann Machine (OGD-RBM), that provides the ability to build and adapt the network architecture of RBM according to the statistics of streaming data. The OGD-RBM is trained in…
New methods improve evaluation of models under varying class imbalance.
problem Optimistic evaluation metrics lead to incorrect conclusions.
method Methods focusing on evaluation under non-constant class imbalance.
result Order of classifiers can change with class imbalance rate.
Proposes methods to improve multi-label learning by addressing local label imbalance.
problem Local label imbalance within minority class examples degrades multi-label learning performance.
method Introduces a measure to assess local label imbalance and two sampling approaches (MLSOL, MLUL) to address it.
result Experimental results show MLSOL and MLUL improve performance on multi-label datasets.
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.
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.
Modeling price dynamics in response to order flow imbalance in Chinese futures markets.
problem Understanding price dynamics in markets with order flow imbalance.
method Modeling order flow imbalance as an Ornstein-Uhlenbeck process with memory and mean-reverting characteristics.
result Horizon-dependent heterogeneity in conventional metrics' interaction with order flow imbalance.
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.