arXiv research

New methods detect unfairness in multiclass classifiers using DCP.

Paper studies multiclass classifiers from binary classifiers, proving methods and demonstrating advantages.

Study proves existence of robust classifiers in multiclass adversarial training.

New method for multiclass classification reduces error bounds.

New framework for evaluating multiclass classifier calibration.

A new method combines simple binary classifiers to build complex multiclass classifiers, achieving performance limits in a Gaussian setting.

A new weighted MCC measure improves classifier performance evaluation.

Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when…

Binary and multiclass epilepsy detection methods using EEG features.

Consistent algorithms for multiclass learning with complex metrics and constraints.

Study builds a classifier for diffusions with unknown diffusion but known drifts.

Develops a robust multiclass classification method for deep image classifiers.

Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and ge…

New research shows SVM and related methods can overfit without harm in multiclass classification.

We prove new fast learning rates for the one-vs-all multiclass plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging drifting distribution. These are two typical scenarios where training data are not iid. The learning rates are obtained under a multiclass vers…

Unified approach to multiclass classification using Gabriel graphs.

Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifi…

Develops methods for valid and validated confidence sets in multiclass and multilabel prediction.

Study resolves conjecture on overparameterized linear models' generalization.

Sparse multinomial logistic regression for multiclass classification with feature selection.

The paper revisits discriminative vs. generative classifiers, showing naive Bayes requires fewer samples.

Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperature scaling, a method to learn a single corrective multiplicative factor for inputs to the last softmax layer. On non-neural models the exist…

Survey on assessing and improving classifier calibration for better decision making.

The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this work to more complex outputs, by providing generalizations of the C-bound to the multiclass and multi-label settings.

We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing $κ$ predominant classes using the labeled training examples such that the proportion of t…

Proposes a stable classifier using inflated argmax for multiclass classification.

Unified SVM framework tackles multiclass and multilabel classification.

We investigate the problem of multiclass classification with rejection, where a classifier can choose not to make a prediction to avoid critical misclassification. First, we consider an approach based on simultaneous training of a classifier and a rejector, which achieves the state-of-the-art performance in the binary …

New method reduces labeler costs by aggregating predictions from local classifiers.

We propose a framework for constructing and analyzing multiclass and multioutput classification metrics, i.e., involving multiple, possibly correlated multiclass labels. Our analysis reveals novel insights on the geometry of feasible confusion tensors -- including necessary and sufficient conditions for the equivalence…

EAST aligns neural network classifiers with user-defined evaluation metrics.

Paper tackles noisy bandit feedback for multiclass classification.

Proposes top-label calibration and M2B framework for multiclass to binary calibration.

Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, and in particular, the number of used features should increase sub-linearly with the number of possible classes. This implies that features …

Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a direct method to optimiz…

New algorithm selects optimal subset for multiclass classifier training.

We develop efficient algorithms to train $\ell_1$-regularized linear classifiers with large dimensionality $d$ of the feature space, number of classes $k$, and sample size $n$. Our focus is on a special class of losses that includes, in particular, the multiclass hinge and logistic losses. Our approach combines several…

New algorithm improves multiclass classification regret bound.

Excessive reuse of holdout data can lead to overfitting. However, there is little concrete evidence of significant overfitting due to holdout reuse in popular multiclass benchmarks today. Known results show that, in the worst-case, revealing the accuracy of $k$ adaptively chosen classifiers on a data set of size $n$ al…

Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two direc…

Improved multiclass classification with class-weighted nearest neighbors.

In this paper, we present a novel approach to construct multiclass classifiers by means of arrangements of hyperplanes. We propose different mixed integer (linear and non linear) programming formulations for the problem using extensions of widely used measures for misclassifying observations where the \textit{kernel tr…

Develops NPMC method for noisy labels, improving multiclass classification accuracy.

The paper sets lower bounds for adversarial robustness in multiclass classification.

New algorithms compute robustness bounds for multiclass classification models.

Simpler classifiers are more robust to adversarial perturbations.

In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters…

Paper analyzes multiclass classification with high-dimensional data.