Probabilistic learning for binary classification with categorical variables.
problem Binary classification with categorical covariates.
method Probabilistic analysis and two algorithms for learning boolean functions.
result Effective learning of boolean functions from binary data.
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.
Quantum circuits represent binary classification trees with binary features.
problem Classifying data using binary classification trees with binary features.
method Quantum circuits and probabilistic approach for traversing decision trees.
result First realization of a decision tree classifier on a quantum device.
The paper discusses building ETF risk models using a multilevel classification taxonomy.
problem Building accurate risk models for ETFs.
method First, build a multilevel classification taxonomy for ETFs. Then, use this taxonomy to define risk factors and build risk models.
result The approach can accurately classify and model ETF risks.
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.
QNNs can't distinguish binary signals from their negations, revealing a new symmetry.
problem Understanding the behavior of QNNs in binary pattern classification.
method Presented and analyzed a new form of invariance (negational symmetry) in QNNs.
result QNNs cannot differentiate a quantum binary signal and its negational counterpart in binary classification tasks.
Study controls error rates of binary classifiers using hypothesis testing.
problem Traditional binary classifiers have uncontrolled error rates.
method Combines binary classification with statistical hypothesis testing.
result Trained classifiers can be made to meet target error rate thresholds.
Proposes an L1-regularized functional SVM for binary classification with functional covariates.
problem Binary classification with multivariate functional covariates.
method L1-regularized functional support vector machine (SVM) with an accompanying algorithm.
result The proposed classifier performs well in prediction and feature selection.
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%.
This paper improves binary classification methods beyond accuracy, especially in imbalanced datasets.
problem Binary classification performance metrics often fail to reflect real-world consequences, especially in imbalanced datasets.
method Derives a generalized Bayes-optimal classifier from accuracy to any performance metric, removing assumptions and providing finite-sample statistical guarantees.
result Optimal classification performance depends on class imbalance properties, providing new insights and guarantees.
Proposes a method to classify binary data from multiple unlabeled datasets.
problem High annotation costs in training classifiers from weakly supervised data.
method Introduces surrogate set classification (SSC) to predict data origin from multiple unlabeled datasets, then uses this to train a binary classifier.
result Demonstrates superior performance compared to existing methods.
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…
New metric reduces arbitrariness in fair binary classification predictions.
problem Variance in predictions leads to arbitrary decisions in fair classification.
method Developed a self-consistency metric and an abstention algorithm.
result Fair binary classification is often close to fair due to variance, not interventions.
Paper introduces ILD algorithm to determine Bayes error for binary classification.
problem Determining the best possible performance in binary classification problems.
method Model-agnostic ILD algorithm to calculate Bayes error.
result Provides intrinsic limits of any binary classification algorithm.
Recently the deep learning techniques have achieved success in multi-label classification due to its automatic representation learning ability and the end-to-end learning framework. Existing deep neural networks in multi-label classification can be divided into two kinds: binary relevance neural network (BRNN) and thre…
Binary classification models get more efficient predictive probabilities.
problem Computing predictive probabilities in Bayesian probit models is computationally challenging.
method Use of expectation propagation (EP) to find a closed-form expression for predictive probabilities.
result Closed-form predictive probabilities improve over existing methods.
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.
New algorithm improves online binary classification with constant time complexity.
problem Online binary classification with rebalancing.
method Non-iteratively reweighted recursive least-squares.
result Exacts converges to batch formulation and outperforms existing algorithms.
Differentially private fair binary classification algorithm developed.
problem Balancing privacy and fairness in binary classification.
method Decoupling technique for fairness, refinement for differential privacy.
result Algorithm maintains fairness, privacy, and utility guarantees.
Reduces bounded loss learning to binary classification.
problem Universal consistency of non-i.i.d. processes with bounded loss.
method Constructive reduction to binary classification.
result Any bounded loss output setting can be reduced to binary classification.
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.
Paper proposes Pcomp classification for binary classification with pairwise confidence comparisons.
problem Lack of pointwise labels due to privacy, confidentiality, or security reasons.
method Developed Pcomp classification, derived an unbiased risk estimator (URE), and improved it using correction functions and consistency regularization.
result Demonstrated the effectiveness of Pcomp classification methods.
Paper shows similarity learning can lead to strong binary classification performance.
problem How similarity learning can lead to good classification performance.
method Product-type formulation of similarity learning is connected to binary classification through an excess risk bound.
result Similarity learning can directly elicit a decision boundary for binary classification.
Paper bounds convergence rate of adversarial surrogate risk.
problem Vulnerability of binary classification models to adversarial attacks.
method Characterizes conditions for adversarial consistency and provides surrogate risk bounds.
result Surrogate risk bounds quantify the rate of convergence of adversarial classification risk.
New tests for binary classification regression functions without distribution assumptions.
problem Testing regression functions in binary classification without distributional assumptions.
method Conditional kernel mean embeddings and resampling-based framework.
result Distribution-free hypothesis tests with exact type I error control.
We show how binary classification methods developed to work on i.i.d. data can be used for solving statistical problems that are seemingly unrelated to classification and concern highly-dependent time series. Specifically, the problems of time-series clustering, homogeneity testing and the three-sample problem are addr…
Sharp bounds on uniform generalization errors in binary linear classification.
problem Understanding the uniform generalization errors in binary linear classification.
method Isoperimetric arguments, Poincaré and log-Sobolev inequalities for joint distributions.
result Sharp concentration bounds on uniform generalization errors, almost sure convergence in broad settings.
Binary classification improves with a small fraction of corrupted labels.
problem Binary classification with corrupted labels.
method Established corruption as a form of regularization and computed upper bounds on estimation error.
result Corruption is beneficial only up to a small fraction of the total sample, scaling with the square root of the sample size.
Proposes MCC-F1 curve for better binary classification evaluation.
problem Misleading performance evaluations with ROC and PR curves for imbalanced data.
method Introduces MCC-F1 curve combining MCC and F1 score.
result MCC-F1 curve provides clearer classifier differentiation.
Paper tackles imbalanced binary classification by optimizing precision and recall directly.
problem Imbalanced binary classification where standard accuracy is misleading.
method Exact constrained reformulations for precision and recall optimization.
result ERO framework outperforms state-of-the-art methods on multiple datasets.
Proposes a new binary classification model inspired by fluid phase separation.
problem Binary classification challenges.
method Discretization of nonlinear reaction-diffusion equation coupled with ODE, inspired by fluid dynamics.
result PSBC model achieves comparable performance to traditional methods on MNIST.
Enhanced binary classifier uses Urysohn's Lemma of Topology.
problem Binary classification challenges.
method Utilizes Urysohn's Lemma of Topology to construct separating functions.
result Exceptional performance in numerical experiments (95% to 100%).
Selective classification improves trading strategies by abstaining from predictions.
problem Designing effective trading strategies using selective classification.
method Extends binary or multi-class classifiers to allow abstaining from predictions, evaluates across different feature sets and classifiers.
result Selective classifiers can improve trading performance by avoiding poor predictions.
New split rules improve subpopulation targeting in policy-making.
problem Improving binary classification for subpopulation targeting in policy-making.
method MDFS, PFS, wEFS for maximizing distance and penalizing final splits.
result Proposed methods target more vulnerable subpopulations than classic CART/KD-CART.
New test for SGD in binary classification reduces computation time.
problem Determining optimal stopping for SGD in binary classification.
method Proposes a new, simple, computationally inexpensive termination criterion for SGD.
result Termination criterion reduces expected misclassification probability.
Paper explores connections between loss functions and consistency in binary classification and regression.
problem Consistency in binary classification and regression applications.
method Characterization of conformable loss functions and derivation of a new Huber-type loss function.
result Margin-based loss functions are equivalent to loss functions of squared standardized logistic regression residuals.
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.
RCAM-based ensemble combines binary classifiers using similarity and vote scheme.
problem Improving binary classification accuracy through ensemble methods.
method RCAM-based ensemble combining classifiers using similarity and recurrent consult-vote scheme.
result RCAM-based ensemble outperforms individual classifiers and majority voting.
Gradient descent with logistic loss can make two-layer networks interpolate binary classification data.
problem Training two-layer networks for binary classification.
method Gradient descent with logistic loss applied to two-layer networks.
result Gradient descent can drive training loss to zero under certain conditions.
Modified AUC improves CNN training by considering model confidence.
problem Improving binary classifier performance metrics.
method Proposes a modified AUC metric that incorporates model confidence into BCE loss for CNN training.
result Demonstrates improved performance on three datasets: MNIST, prostate MRI, and brain MRI.
Comparison of AutoML and DL shows AutoML outperforms simple DL for binary classification.
problem Choosing between AutoML and DL for binary classification tasks.
method Comparison of AutoML and DL on 6 public datasets, evaluation of a new AutoML tool.
result AutoML outperforms simple DL classifiers but integrating DL into AutoML improves performance further.
Can we learn a binary classifier from only positive data, without any negative data or unlabeled data? We show that if one can equip positive data with confidence (positive-confidence), one can successfully learn a binary classifier, which we name positive-confidence (Pconf) classification. Our work is related to one-c…
Study finds AUC is most consistent across different prevalence in binary classification.
problem Consistency of model evaluation metrics across varying prevalence in binary classification.
method Analysis of 156 data scenarios with 18 metrics, 5 models, and a random guess model.
result AUC has the smallest variance in evaluating individual models and ranking of models.
Study proposes a differentiable surrogate loss function for optimizing Fβ score in binary classification with imbalanced data.
problem Non-differentiability of Fβ score makes it unsuitable for optimization by gradient-based learning. method Investigated relationship between Fβ score and loss functions, proposed a differentiable surrogate loss function. result Gradient paths of the proposed surrogate Fβ loss function approximate the gradient paths of the Fβ score. Study on double descent behavior in two-layer neural networks for binary classification.
problem Understanding the double descent phenomenon in model test error.
method Two-layer neural network with ReLU activation for binary classification. Quantified model size by sample-to-dimension ratio. Empirical risk minimization using Convex Gaussian Min Max Theorem.
result Observed and investigated the double descent behavior of model test error.
New method corrects skewed confidence for PbN classification.
problem Weakly supervised binary classification with biased negative data.
method Corrects skewed confidence in negative data to improve classifier.
result Reduces distortion in posterior probability for PbN classification.
Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time-consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability…
Study improves learning algorithms for convex polyhedra in Hilbert spaces.
problem Learning convex polyhedra in Hilbert spaces.
method Proposes an algorithm for learning a polyhedron in a Hilbert space.
result Correctly classifies at least 1-ε of the distribution with high probability.