Study examines how classifier performance is affected by training data quality.
problem How classifier performance is affected by training data quality.
method Extensive numerical experiments with four classifiers (Bayes, neural nets, partition models, random forests) on metagenomic assembly data.
result Classifier performance degrades as training data quality degrades, leading to breakdown-like behavior.
Ensemble quantile classifier improves performance on high-dimensional data.
problem Discriminating high-dimensional data with heavy-tailed or skewed inputs.
method Regularized quantile classifier that assigns variable weights.
result Consistently estimates minimal population loss and is Bayes optimal.
Unsupervised classification methods learn a discriminative classifier from unlabeled data, which has been proven to be an effective way of simultaneously clustering the data and training a classifier from the data. Various unsupervised classification methods obtain appealing results by the classifiers learned in an uns…
Topological data analysis classifies encrypted bits with success.
problem Classifying encrypted data with traditional machine learning methods.
method Persistent homology for generating topological features, machine learning pipeline.
result Successfully classifies encrypted data, outperforming classical models.
New classifiers converge under large data, simplifying complex models.
problem Complex predictive models under large datasets.
method Convergence of simultaneous and marginal classifiers under partition exchangeability.
result Asymptotic convergence of classifiers with large data reduces computational complexity.
ECPF improves classification accuracy and speed for evolving data streams.
problem Reusing classifiers trained on recurring concepts to maintain accuracy and speed.
method ECPF uses similarity of classifications to quickly identify the best classifier to reuse.
result ECPF significantly outperforms state-of-the-art frameworks on synthetic and real-world datasets.
A review of statistical SSL methods showing improved classifier performance.
problem Forming classifiers from limited labeled data and many unlabeled data.
method Statistical approaches to semi-supervised learning.
result A classifier from partially labeled data can have lower expected error rate.
We consider classifiers for high-dimensional data under the strongly spiked eigenvalue (SSE) model. We first show that high-dimensional data often have the SSE model. We consider a distance-based classifier using eigenstructures for the SSE model. We apply the noise reduction methodology to estimation of the eigenvalue…
diproperm tests differences in HDLSS data with binary classifiers.
problem Testing differences in HDLSS data with binary classifiers.
method DiProPerm test for binary linear classifiers.
result Validates the DiProPerm test on real-world data.
Better calibration for small datasets improves probability estimates.
problem Limited data hinders traditional calibration methods.
method Generating additional calibration data improves classifier performance.
result The proposed approach enhances calibration for small datasets.
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.
We introduce a nonlinear aggregation type classifier for functional data defined on a separable and complete metric space. The new rule is built up from a collection of M arbitrary training classifiers. If the classifiers are consistent, then so is the aggregation rule. Moreover, asymptotically the aggregation rule b…
Develops an online nonparametric classifier for massive data.
problem Challenges of batch kernel-based nonparametric classifiers in massive data.
method Online principle components analysis to reduce dimensionality, followed by stochastic approximation algorithm for real-time calculation.
result Online classifier provides the best trade-off between accuracy and computation cost.
Fuzzy eIX method evolves classifiers for online data streams.
problem Handling time-varying classifiers in online data streams.
method Develops evolving Internal-eXternal Fuzzy granules for numerical data.
result Fuzzy eIX maintains high accuracy in dynamic scenarios.
Algorithm estimates bounds of updated classifier coefficients efficiently.
problem Determining sensitivity of updated classifiers without retraining.
method Proposes an algorithm to estimate upper and lower bounds of updated classifier coefficients.
result Estimates bounds with low computational complexity and tightness.
The Era of Big Data has forced researchers to explore new distributed solutions for building fuzzy classifiers, which often introduce approximation errors or make strong assumptions to reduce computational and memory requirements. As a result, Big Data classifiers might be expected to be inferior to those designed for …
A new method for functional data classification using supervised manifold learning.
problem Improving classification of functional data.
method Supervised manifold learning approach considering label information.
result The proposed method achieves highly competitive performance compared to existing methods.
We compare and contrast two approaches to validating a trained classifier while using all in-sample data for training. One is simultaneous validation over an organized set of hypotheses (SVOOSH), the well-known method that began with VC theory. The other is withhold and gap (WAG). WAG withholds a validation set, trains…
Machine learning has been applied to a broad range of applications and some of them are available online as application programming interfaces (APIs) with either free (trial) or paid subscriptions. In this paper, we study adversarial machine learning in the form of back-box attacks on online classifier APIs. We start w…
New uniqueness concept for adversarial Bayes classifier.
problem Understanding adversarial Bayes classifiers in binary classification.
method Developed a new notion of uniqueness and analyzed it for a family of one-dimensional data distributions.
result Improved regularity of adversarial Bayes classifiers as perturbation radius increases.
Paper develops robust SVM classifiers for uncertain data.
problem Sensitivity of SVM classifiers to data uncertainty.
method Two probabilistic approaches: Single Perturbation and Extreme Empirical Loss.
result Both methods reduce data uncertainty effects efficiently.
Improves multi-label classification with a new network model.
problem Improving multi-label classification accuracy.
method Introduces Classifier Chain Network (CCN) for multi-label classification.
result CCN outperforms benchmark methods in simulations and real data.
Distillation speeds up classifier training and provides insights into its success.
problem Empirical success of knowledge distillation without theoretical explanation.
method Study of linear and deep linear classifiers, proving a generalization bound.
result Three key factors for distillation success: data geometry, optimization bias, strong monotonicity.
This work addresses fairness in ML models by training and evaluating attribute classifiers under uncertain and incomplete data.
problem Challenges in fairness metrics due to uncertain and incomplete data.
method Developed a theoretical and empirical analysis to understand and improve bias estimation in the data-scarce regime.
result The test accuracy of the attribute classifier is not always correlated with its effectiveness in bias estimation.
This paper proposes a systematic framework to design a classification model that yields a classifier which optimizes a utility function based on prior knowledge. Specifically, as the data size grows, we prove that the produced classifier asymptotically converges to the optimal classifier, an extended version of the Bay…
Mapper classifier improves robustness over CNNs.
problem Improving robustness of classifiers against attacks.
method Mapper algorithm applied to PCA or autoencoders projected data.
result Mapper classifier is immune to gradient-based attacks.
Paper introduces a method to evaluate abstaining classifiers by considering missing predictions as counterfactuals.
problem Lack of a principled approach to evaluate and compare abstaining classifiers, especially when missing predictions are important.
method Develops a novel approach to treat abstentions as missing data, defining counterfactual scores and using causal inference methods to estimate them.
result Shows that under certain conditions, counterfactual scores can be identified and estimated efficiently, improving evaluation of abstaining classifiers.
A new classifier improves one-class predictions on unevenly sampled data.
problem Non-uniformly sampled data affects one-class classifier performance.
method Dynamic decision boundary based on minimum spanning tree.
result Proves effectiveness and robustness compared to state-of-the-art classifiers.
It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper demonstrates that an ensemble classifier, Diversified Multiple Tree (DMT), is more robust in classifying noisy data than other widely used ensemble methods. DMT is teste…
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.
Discriminative classifier for compositional data using hierarchical mixture of Generalized Dirichlet models.
problem Classifying compositional data, especially in spam detection and color space identification.
method Hierarchical mixture of discriminative Generalized Dirichlet classifiers, using variational approximation for parameter learning.
result First time a variational upper-bound for Generalized Dirichlet mixture is proposed in literature.
As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversary …
This paper addresses the general problem of accurate identification of oil reservoirs. Recent improvements in well or borehole logging technology have resulted in an explosive amount of data available for processing. The traditional methods of analysis of the logs characteristics by experts require significant amount o…
A new PU classifier PUAL tackles trifurcate data issues.
problem Training classifiers on trifurcate data containing only labeled-positive instances and unlabeled instances.
method PUAL classifier with asymmetric loss and kernel-based algorithm.
result PUAL achieves satisfactory classification on trifurcate data.
New classifiers for HDLSS data classify without tuning, robustly.
problem Classification of high-dimensional data with small samples.
method Data-adaptive energy distance classifiers, free of tuning parameters.
result Perfect classification in HDLSS asymptotic regime under general conditions.
A novel distributed adaptive NN classifier for large data sets.
problem Handling large and distributed data for efficient classification.
method Distributed adaptive nearest neighbor classifier with stochastic tuning parameter selection and early stopping rule.
result Achieves nearly optimal convergence rate under large sub-sample sizes.
The cellular tree classifier model addresses a fundamental problem in the design of classifiers for a parallel or distributed computing world: Given a data set, is it sufficient to apply a majority rule for classification, or shall one split the data into two or more parts and send each part to a potentially different …
Improved classifier for PU data using logistic regression.
problem Analysis of Positive Unlabeled data under SCAR assumption.
method Fitting misspecified logistic regression model to PU data.
result The classifier performs on par or better than competitors on real data sets.
Enhances multi-tag classification using low-dimensional vector representations and virtual data.
problem Improving the performance of multi-tag classifiers.
method Embedding raw data into a low-dimensional feature space, then generating virtual data from linear operations on these vectors, to train multi-tag classifiers.
result Significant improvement in F1 scores (up to 224%) compared to training directly with raw data.
Linear classifiers in product space forms improve scRNA-seq data classification.
problem Linear classification in products of Euclidean, spherical, and hyperbolic spaces.
method Novel formulations of linear classifiers on Riemannian manifolds, proving expressive power, and formalizing perceptron and SVM classifiers.
result Linear classifiers in product space forms have the same expressive power as in Euclidean space of the same dimension.
A new metric estimates classifier accuracy using only training data.
problem Assessing classifier accuracy without cross-validation.
method Bayesian Area Under the ROC Curve (CBAUC) metric for linear classifiers.
result The CBAUC is faster and more accurate than conventional AUC estimators.
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatica…
In a broad range of classification and decision making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier accuracy can be assessed using available labe…
In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of the original training data. Consequentially, the so-called source classifier, trained on the available labelled data, deteriorates on the test, or target, data. Domain adaptive classifiers aim to combat this probl…
The variational autoencoder (VAE) is a popular probabilistic generative model. However, one shortcoming of VAEs is that the latent variables cannot be discrete, which makes it difficult to generate data from different modes of a distribution. Here, we propose an extension of the VAE framework that incorporates a classi…
Paper analyzes mistake and generalization of MNIC classifiers.
problem Understanding the performance of interpolating classifiers.
method Elementary analyses of MNIC's regret and generalization.
result MNIC generalizes with a rate proportional to the norm of the interpolating solution and inversely proportional to the number of data points.
We develop a worst-case analysis of aggregation of classifier ensembles for binary classification. The task of predicting to minimize error is formulated as a game played over a given set of unlabeled data (a transductive setting), where prior label information is encoded as constraints on the game. The minimax solutio…
Paper explores Bayes rule for Gaussian mixtures with missing data, outperforming supervised classifiers.
problem Improving classification accuracy in partially classified samples with missing data.
method Generative model framework with missing-data mechanism, Bayes rule allocation.
result Bayes rule classifier with missing-data mechanism outperforms fully supervised classifiers in various conditions.