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.
The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multi-class classification…
A new method learns features for one-class classification using intra-class splitting.
problem Challenges in one-class classification due to limited normal class samples.
method Intra-class splitting and joint training of typical and atypical samples with loss functions.
result The method outperforms other models in one-class classification tasks.
Extends binary data classification for hierarchical data.
problem Efficiently classify data with hierarchical class labels.
method Extends a simple binary data classification approach to handle hierarchical relationships.
result Shows computational and accuracy advantages in certain settings.
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.
Stochastic mirror descent improves multi-class classification accuracy.
problem Efficient multi-class classification using stochastic optimization.
method Derive risk bounds for stochastic mirror descent algorithm.
result Algorithm provides efficient error bounds for classification.
New approach uses class domains for classification when distributions are unknown.
problem Traditional classification rules are inadequate when class distributions are ill-defined or unknown.
method Use class domains instead of class distributions for constructing a reliable decision function.
result Illustrated examples show the effectiveness of the new approach.
DOC3 learns from contradictions to improve deep one class classification.
problem Deep one class classification problems.
method Formalizes learning from contradictions for one class large-margin loss, proposes DOC3 algorithm.
result DOC3 incurs lower generalization error compared to traditional inductive learning.
Zero-shot audio classification using class label embeddings.
problem Classifying audio without labeled data.
method Bilinear model with audio feature embeddings and class label embeddings.
result Achieved accuracy up to 39.7% for natural audio categories.
CCCDs tackle class imbalance in classification.
problem Class imbalance in statistical classification.
method Class cover catch digraphs (CCCDs) for graph theoretic solutions.
result CCCD classifiers perform well in class imbalance scenarios.
Proposes DNCM model for efficient incremental odor classification.
problem Dynamic growth of odor classes over time.
method Deep learning framework and nearest class mean method.
result Efficient for integrating new classes with limited examples.
Visualizes classification results with class maps.
problem Label bias in classification predictions.
method Class maps reflecting probability, distance, and mislabeling likelihood.
result Insight into classification results and data structure.
Paper generalizes PU classification for class prior shift and asymmetric error scenarios.
problem Bottlenecks in binary classification from PU data due to test marginal distribution and equal error penalties.
method Analysis of Bayes optimal classifier, risk minimization framework, and density ratio estimation framework.
result PU classification under class prior shift is equivalent to PU classification with asymmetric error.
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.
Three methods combine one-class classifiers with MST-CD and N-ary Trees for binary classification.
problem Binary classification with overlapping and imbalanced classes.
method Combining one-class classifiers with MST-CD and N-ary Trees to handle inconsistencies and spurious connections.
result The proposed methods are feasible and comparable to state-of-the-art algorithms.
Estimates neural network errors for classification problems.
problem Binary and multi-class classification problems.
method Rademacher complexity estimates and direct approximation theorems.
result A priori error estimates for regularized loss functionals.
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%.
Random Forests are extended to one-class classification.
problem No extension of RFs to one-class setting.
method Natural methodology to extend splitting criteria.
result Empirical demonstration of relevance of approach.
SWRLDA improves LDA for multi-class classification with edge classes.
problem LDA's vulnerability to edge classes causing biased mean and large distances.
method Self-weighted robust LDA with l21-norm distance criterion.
result SWRLDA outperforms other methods on synthetic and real-world datasets.
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.
Improved multiclass classification with class-weighted nearest neighbors.
problem Multiclass classification with large or imbalanced classes.
method Class-weighted k-nearest neighbors algorithm, derived bounds on accuracy and risk.
result Optimized classification metrics like F1 score or Matthew's Correlation Coefficient.
A double sampling strategy reduces multi-class to binary classification for large-scale text data.
problem Efficiently handling large-scale multi-class classification with long-tailed class distributions.
method Double sampling strategy on top of multi-class to binary reduction.
result The proposed approach reduces training and prediction time, memory consumption, and improves predictive performance.
KFHE uses Kalman filters to improve ensemble classification accuracy.
problem Improving multi-class ensemble classification accuracy.
method KFHE treats ensemble training as a state estimation problem using Kalman filters.
result KFHE outperforms state-of-the-art algorithms in noisy and clean datasets.
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.
Paper improves anomaly detection methods for one-class classification.
problem Challenges in selecting kernels for anomaly detection.
method Generalizes kernel selection methods from binary to one-class classification.
result Extensive comparison of kernel selection methods.
C-HMCNN(h) improves HMC classification by leveraging class hierarchy.
problem Hierarchical multi-label classification with class hierarchy constraints.
method Exploits class hierarchy to produce coherent predictions for multi-label classification.
result C-HMCNN(h) outperforms state-of-the-art models in HMC classification.
Matrix sketching balances class sizes for better supervised classification performance.
problem Class imbalance in supervised classification leads to poor performance.
method Matrix sketching using random projections to rebalance class sizes.
result Rebalanced classes improve classification performance, especially for minority classes.
Binary classifier learned from only positive data with confidence.
problem Learning binary classifier from only positive data.
method Simple empirical risk minimization framework, model-independent and optimization-independent.
result Consistency and estimation error bound established for Pconf classification.
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 presents Label Mapping method to solve large-scale multi-class classification problems.
problem Large-scale multi-class classification problems in deep neural networks.
method Label Mapping (LM) method to decompose the problem into smaller sub-problems.
result LM significantly outperforms standard methods in terms of accuracy and model complexity.
Study extends learnability equivalence to multi-class and regression, overcoming binary classification limits.
problem Equivalence of online and private learnability in multi-class and regression settings.
method Introduced a novel Littlestone dimension variant and threshold functions for multi-class classification.
result Online learnability implies private learnability in multi-class classification but not in regression.
Directly compute classification by learning features with class scores.
problem Classification efficiency and accuracy on various datasets.
method PCA for feature encoding, supervised learning model with encoder-decoder structure.
result Effective classification performance on multiple datasets.
This paper reviews resampling techniques to improve classification in imbalanced datasets.
problem Improving classification performance in datasets with class imbalance.
method Review and study of various resampling techniques.
result Effectiveness of resampling techniques on classification performance.
CE-GAN improves deep learning for imbalanced data classification.
problem Difficulty in recognizing minority classes in imbalanced data.
method Class Expert Generative Adversarial Network (CE-GAN) architecture modification.
result CE-GAN provides better performance for imbalanced data classification.
Proposes multi-neighborhood LBPs for land use classification.
problem Challenges in classifying land use images due to intra class variability and inter class similarities.
method Uses multi-neighborhood LBPs combined with nearest neighbor classifier.
result Achieved an accuracy of 77.76% on UC Merced 21 class land use image dataset.
Deep reinforcement learning improves classification accuracy for imbalanced datasets.
problem Imbalanced datasets challenge conventional classification algorithms.
method Formulated as a sequential decision-making process, solved using deep Q-learning network.
result Proposed model outperforms other imbalanced classification algorithms.
A novel method learns representations from PU data without needing class-prior estimation.
problem Training classifiers from only positive and unlabeled data requires accurate class-prior probability estimation.
method Information-theoretic representation learning based on the information-maximization principle.
result Our method combined with deep neural networks achieves state-of-the-art PU classification performance.
Paper reduces neural network complexity for image classification.
problem High computational complexity in deep neural networks.
method Proposes a two-step classification process: coarse-grain and fine-grain.
result Achieves similar accuracy with less computational complexity.
A new active learning method for one-class classification using two classifiers.
problem Reducing manual labeling efforts in one-class classification.
method Uses two one-class classifiers for active learning, proposing new query strategies.
result Improved results compared to existing methods on various datasets.
Paper addresses class imbalance in disk SMART dataset using GANs and genetic algorithms.
problem Class imbalance in disk SMART dataset.
method Data synthesised by multivariate GANs mixed with genetic algorithms.
result Higher disk fault classification prediction accuracy.
A new classification method using class-specific features for improved text categorization.
problem Improving text categorization accuracy by leveraging class-specific features.
method EEF classifier based on class-specific features and optimal Bayesian classification rule.
result The proposed EEF classifier outperforms conventional methods on real-life data sets.
Unified approach to non-standard classification tasks.
problem Non-standard classification tasks like semi-supervised, positive-unlabelled, multi-positive-unlabelled and noisy-label learning.
method Probabilistic, unified approach training a classifier to predict label-distributions, then inferring class-distributions.
result Unified model for various non-standard classification tasks.
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.
A new framework improves few-shot classification by learning to generalize to unseen classes.
problem Training metric-based meta-learning approaches for few-shot classification often fails to generalize to unseen classes.
method Proposes a bilevel optimization framework to explicitly constrain meta-training to reduce unseen classification error.
result Significantly improves performance on unseen classes compared to episodic training.
Paper investigates methods to improve classification by inducing a hierarchy from flat labels.
problem Improving classification performance on datasets lacking a natural hierarchy.
method The approach involves clustering conditional distributions and using a hierarchical classifier with the induced hierarchy.
result The methods can discover latent hierarchies and improve accuracy in various applications.
Two new undersampling methods improve classification accuracy for imbalanced datasets.
problem Class imbalance and distributional differences in large datasets lead to biased models and poor predictive performance.
method Mutual information-based stratified simple random sampling and support points optimization.
result Empirical results show higher balanced classification accuracy compared to traditional techniques.
Study identifies pitfalls in assessing hierarchies for multi-class classification.
problem Lack of understanding in selecting hierarchies for multi-class classification.
method Analyzed and compared popular approaches to extracting hierarchies.
result Hierarchy quality becomes irrelevant when using powerful classifiers.
This work improves metric learning models by incorporating class hierarchies.
problem Class hierarchies are often ignored in classification-based metric learning models.
method Trained softmax classifier and metric learning models with predefined class hierarchies.
result ProxyDR model performs better in hierarchical inference and hierarchy-informed performance.