Survey of methods for classifier comparison using precision.
problem Lack of methods for classifier comparison using precision.
method Statistical methods for precision comparison, accounting for inter-precision correlation.
result Methods to test global null hypothesis of model comparison using precision.
TripletBoost learns classifiers from noisy triplet comparisons.
problem Learning from comparison-based data.
method Aggregate weak classifiers from weakly learned triplets, then boost.
result Theoretical guarantees and empirical competitiveness.
Review of LVQ classifiers, proposing a taxonomy and comparing eleven approaches.
problem Improving LVQ classifiers through taxonomy and comparison.
method Taxonomy integration, comparison of eleven LVQ classifiers.
result Comparison of eleven LVQ classifiers on real-world and artificial datasets.
New method compares classifiers using GSD-front, addressing statistical uncertainty and robustness.
problem Comparing classifiers with multiple quality metrics and statistical uncertainty.
method Proposes GSD-front and statistical tests for robust comparisons.
result Reliable method for comparing classifiers with statistical uncertainty and robustness.
Paper proposes a new method to compare classifiers across multiple datasets.
problem Comparing classifiers over multiple datasets with multiple criteria.
method Adopting decision theory, the paper introduces generalized stochastic dominance for ranking classifiers.
result Generalized stochastic dominance can be used to rank classifiers and statistically tested.
Active learning with comparisons reduces label needs for linear classifiers.
problem Learning with limited labeled data in big data.
method Active learning with comparison queries, RPU learning model.
result Active learning with comparisons requires exponentially fewer samples for linear classifiers.
New depth function for partial orders helps compare machine learning algorithms.
problem Comparing machine learning algorithms using non-standard data types.
method Adapted simplicial depth to partial orders, using ufg depth for comparison.
result Demonstrates promising variety of analysis approaches based on ufg methods.
Bayesian model compares classifier accuracies across multiple datasets.
problem Shortcomings of null hypothesis significance tests in comparing classifier accuracies.
method Bayesian hierarchical model analyzing cross-validation results.
result Posterior probability of classifier accuracies being equivalent or different.
New algorithms learn from comparisons to classify data robustly to noise.
problem Learning robust classifiers from noisy data efficiently.
method Introducing comparison queries to active learning, providing noise-tolerant classifiers.
result First time and query efficient algorithms for robust learning under bounded noise.
Paper proposes an unbiased classifier from triplet comparison data.
problem Learning a classifier from triplet comparison data.
method Empirical risk minimization framework with an unbiased estimator.
result The proposed method achieves better performance than baseline methods.
Elicit performance metrics from classifier comparisons.
problem Discover the performance metric a practitioner prefers for binary classification.
method Formalize and exploit geometric properties of confusion matrices for efficient metric elicitation.
result Provably efficient algorithms for eliciting linear and linear-fractional metrics from pairwise feedback.
Paper tackles interactive learning with noisy labels and comparisons, showing how comparison oracles can improve learning efficiency.
problem Interactive learning with noisy labels and comparisons.
method Characterizes comparison oracle as threshold function learning problem, presents algorithm with query complexity analysis.
result Lower bounds show almost optimal label and total query complexity.
Develops a theoretical framework for fair feature selection method evaluation.
problem Unfair comparisons between feature selection methods due to varying evaluation methods.
method Theoretical framework based on mutual information for feature ordering.
result Undoubtful comparison of feature selection methods independent of estimation methods, classifiers, and datasets.
The problem of supervised classification (or discrimination) with functional data is considered, with a special interest on the popular k-nearest neighbors (k-NN) classifier. First, relying on a recent result by Cerou and Guyader (2006), we prove the consistency of the k-NN classifier for functional data whose distribu…
The article explains how to estimate confusion matrices for classifiers using unlabeled data.
problem Estimating sensitivity and specificity of binary medical diagnostic tests without gold standard tests.
method Modifying diagnostic test solutions to estimate confusion matrices for classifiers on unlabeled data.
result The approach can be used to estimate accuracy statistics for supervised or unsupervised binary classifiers on unlabeled data.
ConfusionFlow visualizes classifier confusion over time for model comparison.
problem Insufficient performance analysis of classifiers.
method Interactive, model-agnostic visualization tool combining confusion matrices and temporal analysis.
result ConfusionFlow facilitates detailed, comparative analysis of classifier performance over time.
Project compares KDDCup99 and NSL-KDD datasets using ML classifiers.
problem Quality and bias in IoT network intrusion detection datasets.
method Evaluation of various ML classifiers on both datasets.
result NSL-KDD dataset is of higher quality with lower bias.
DMT improves accuracy on noisy biomedical data.
problem Noisy data affects classification accuracy.
method Diversified Multiple Tree (DMT) ensemble classifier.
result DMT outperforms other classifiers on noisy data.
The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assum…
Paper explains classifier predictions without classifier or data info.
problem Explain classifier predictions without classifier or data info.
method Instance-based approach using Growing Spheres algorithm with sparsity constraint.
result Minimal changes needed to alter a prediction identified.
New method certifies deep graph classifiers with tighter risk bounds.
problem Certifying the reliability of deep graph classifiers.
method Linearized deep assignment flows with random initial conditions, using PAC-Bayes risk certification.
result Computes tighter out-of-sample risk certificates efficiently.
Paper studies convergence rates from surrogate risk minimizers to Bayes optimal classifier.
problem Analyzing the convergence rates of surrogate risk minimizers to the Bayes optimal classifier.
method Introducing consistency intensity to characterize surrogate loss functions and using it to derive convergence rates.
result Empirical surrogate risk minimizers converge faster to the Bayes optimal classifier under certain conditions.
Paper proposes a new classifier for hyperbolic spaces using horospherical boundaries.
problem Optimization of large margin classifiers in hyperbolic spaces.
method Horospherical decision boundaries for geodesically convex optimization.
result Geodesically convex optimization leads to globally optimal solutions.
The paper uses geometric methods to classify medical data histograms.
problem Classifying medical data histograms for disease diagnosis.
method Information geometry of beta distributions for comparing and classifying histograms.
result Geometric tools, particularly negatively curved Fisher information, enable unique mean calculation and K-means classification.
In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that c arries out a greedy search through the space of features. We hyp…
Paper investigates personalization in emotion recognition from physiological data.
problem Emotion recognition from physiological signals.
method Features are extracted and ranked based on their effect on classification accuracy. Different classifiers are compared. Inter-subject variability and personalization effect are investigated through trial-based and subject-based cross-validation. A personalized model is introduced.
result Personalized model enhances emotional state prediction.
Enhances classifier performance through feature space transformations and model selection.
problem Improving the accuracy of classifiers by reducing complexity.
method Combining feature mapping, prototype selection, and kernel function transformations to transform data into a more convenient distribution.
result Our methods produce competitive classifiers and are statistically different among them.
This article proposes a novel density estimation based algorithm for carrying out supervised machine learning. The proposed algorithm features O(n) time complexity for generating a classifier, where n is the number of sampling instances in the training dataset. This feature is highly desirable in contemporary applicati…
A new adaptive kNN classifier outperforms Random Forests.
problem Improving classification accuracy using nearest neighbors.
method Finding discriminant subspaces for efficient nearest neighbor classification, leveraging bagging for diversity.
result The proposed method outperforms Random Forests and other nearest neighbors ensembles.
Delta method vs Bootstrap for deep learning classification shows strong linear relationship and faster computation.
problem Validating the Delta method for deep learning classification.
method Comparison of Delta method and Bootstrap on LeNet-based neural networks using MNIST and CIFAR-10 datasets.
result The Delta method provides a five times faster computation with strong linear predictive uncertainty relationship.
This paper describes a time-series-based classification approach to identify similarities between bio-medical-based situations. The proposed approach allows classifying collections of time-series representing bio-medical measurements, i.e., situations, regardless of the type, the length and the quantity of the time-ser…
Improves classifier evaluation by aligning with Total Classification Cost.
problem Lack of consensus on evaluation metrics and class imbalance issues.
method Introduces Weighted Accuracy (WA) and a reweighting framework for cost-sensitive scenarios.
result WA aligns with Total Classification Cost (TCC) minimization under realistic conditions.
Researchers combined linear classifiers using score functions and found simple and trimmed averages to be the best combination strategies.
problem Combining linear classifiers using their score functions.
method Two score functions tested; four combination strategies investigated; comparison with majority voting and model averaging.
result Simple and trimmed average combination strategies were the best.
Generative text classifiers are most vulnerable to membership inference attacks.
problem Privacy threat from Membership Inference Attacks on generative text classifiers.
method Comprehensive empirical evaluation of generative, discriminative, and pseudo-generative classifiers across various datasets.
result Generative classifiers explicitly modeling P(X,Y) are most vulnerable to membership leakage. Study compares chi-squared divergence and KL-divergence posteriors for PAC-Bayesian bounds.
problem Investigates optimal posteriors for PAC-Bayesian bounds using chi-squared divergence.
method Analyzes bounds for three distance functions, derives FP equations for computation.
result Chi-squared divergence based posteriors have weaker bounds and worse test errors.
Self-consistency improves the accuracy of model comparison methods.
problem Improving the accuracy of model comparison methods when simulation models are misspecified.
method Supplement traditional simulation-based training with a self-consistency loss on unlabeled real data.
result Self-consistency training improves model comparison accuracy, especially in open-world scenarios.
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.
This paper compares different activation functions in GLVQ models.
problem Improving performance of GLVQ models using different activation functions.
method Investigates and compares ReLU, sigmoid, and swish activation functions in GLVQ models.
result Different activation functions have varying impacts on GLVQ model performance.
Jointly optimizes domain alignment and classifier learning for unsupervised domain adaptation.
problem Transfer classifier from source to target domain without labeled data.
method Jointly optimizes semantic domain alignment and target classifier learning.
result Joint optimization yields best performance compared to state-of-the-art methods.
This article compares and evaluates active learning methods for outlier detection with one-class classifiers.
problem Selecting an appropriate active learning method for outlier detection with one-class classifiers is challenging due to diverse assumptions and objectives.
method The article categorizes various methods, proposes evaluation criteria, and conducts extensive experiments to compare them.
result Formulated guidelines for selecting active learning methods for outlier detection with one-class classifiers.
Decision making from data involves identifying a set of attributes that contribute to effective decision making through computational intelligence. The presence of missing values greatly influences the selection of right set of attributes and this renders degradation in classification accuracies of the classifiers. As …
Neyman-Pearson testing improves goodness of fit in detecting new physics.
problem Detecting small anomalies in data distributions.
method Employing Neyman-Pearson strategy with a rich parametrized family of models.
result Neyman-Pearson testing is more sensitive to small departures and unbiased towards specific anomalies.
We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…
A meta-learning framework for dynamic ensemble selection.
problem Improving dynamic ensemble selection for classification tasks.
method Proposes a three-step framework using meta-learning to select competent classifiers.
result Problem-dependent scenario yields the best performance.
We introduce a new metric to evaluate corruption robustness of ML classifiers.
problem Evaluating corruption robustness of machine learning classifiers.
method We propose a test data augmentation method using minimal class separation distance to derive a robustness distance ε and a metric MSCR.
result The MSCR metric allows interpretable comparison of classifier robustness on different datasets.
Paper compares two multi-label classification methods: Bayes metaclassifier and soft-confusion-matrix.
problem Comparing multi-label classification methods under the same framework.
method Bayes metaclassifier combined with problem-transformation approach, and soft-confusion-matrix classifier.
result Both methods perform similarly, but statistical analysis provides insights.
New oracle uses uncertainty for active classification with noisy feedback.
problem Improving query complexity in interactive binary classifier learning.
method Proposes a new pairwise comparison oracle that considers uncertainty and an adaptive labeling algorithm.
result Demonstrates improved performance and efficiency compared to existing methods.
Flexible classifier using Mahalanobis distances for non-elliptical distributions.
problem Classifying non-elliptical and multimodal distributions.
method Semiparametric classifier based on Mahalanobis distances and generalized additive models.
result The proposed classifiers outperform traditional methods in high-dimensional, low-sample-size scenarios.