arXiv research

Paper proposes a method to select base classes for few-shot learning.

A new data augmentation method selects mixed classes based on class distances for better performance.

Class selectivity affects robustness to corruptions but not to adversarial attacks.

Introduces model class selection to find sets of near-optimal models.

A new framework for selecting base classes in multi-class classification boosts accuracy.

Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowle…

In this paper, a novel feature selection method is presented, which is based on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To better capture the relationship between features and the class, class labels are separated into individual variables and relevance and redundancy are explicitly handle…

Feature selection is one of the most fundamental problems in machine learning. An extensive body of work on information-theoretic feature selection exists which is based on maximizing mutual information between subsets of features and class labels. Practical methods are forced to rely on approximations due to the diffi…

Paper introduces a new performance metric for class imbalance datasets.

Conventional mutual information (MI) based feature selection (FS) methods are unable to handle heterogeneous feature subset selection properly because of data format differences or estimation methods of MI between feature subset and class label. A way to solve this problem is feature transformation (FT). In this study,…

A novel method selects genes for high-dimensional gene expression data with class imbalance.

An ensemble technique is characterized by the mechanism that generates the components and by the mechanism that combines them. A common way to achieve the consensus is to enable each component to equally participate in the aggregation process. A problem with this approach is that poor components are likely to negativel…

New algorithm offers costless model selection in contextual bandits.

Random Forest variable importance is improved by class balancing techniques.

New algorithms for model selection in linear bandits adapt to instance complexity.

Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is "cancerous" or "healthy" from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a ke…

Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class. The Frienemy Indecision REgion DES (FIRE-DES) tackles this problem by pre-selecting classifiers that correc…

Proposes a new tree-based algorithm for class-imbalanced data.

Regularizing for or against class selectivity in DNNs improves test accuracy.

Lower class selectivity makes networks more robust to natural perturbations but more vulnerable to adversarial attacks.

Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential sub-optimality of ranking based feature selection by any criterion, we …

New gene selection method improves tumor classification accuracy.

Selective classification improves trading strategies by abstaining from predictions.

Tree tensor networks balance model complexity and empirical risk for high-dimensional function approximation.

A new algorithm selects models for contextual bandits, reducing regret.

Study on selecting between base algorithms in stochastic bandit problems.

Multiple kernel learning (MKL) algorithms combine different base kernels to obtain a more efficient representation in the feature space. Focusing on discriminative tasks, MKL has been used successfully for feature selection and finding the significant modalities of the data. In such applications, each base kernel repre…

New method proves neural networks can select features consistently.

The class of chain event graph models is a generalisation of the class of discrete Bayesian networks, retaining most of the structural advantages of the Bayesian network for model interrogation, propagation and learning, while more naturally encoding asymmetric state spaces and the order in which events happen. In this…

A neural network approach for feature selection using mutual information.

New algorithms improve model selection in linear bandits with optimal regret.

Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take plac…

One crucial aspect of partial domain adaptation (PDA) is how to select the relevant source samples in the shared classes for knowledge transfer. Previous PDA methods tackle this problem by re-weighting the source samples based on their high-level information (deep features). However, since the domain shift between sour…

ADML combines debiased learning with data-driven model selection for efficient inference.

A new method for selective classification trades off accuracy for coverage.

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment ca…

Adaptive model selection for RL with unknown function classes.

Many natural signals exhibit a sparse representation, whenever a suitable describing model is given. Here, a linear generative model is considered, where many sparsity-based signal processing techniques rely on such a simplified model. As this model is often unknown for many classes of the signals, we need to select su…

Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various classes of methods, forward feature selection methods based on …

New unsupervised feature selection method for imbalanced datasets.

Prototype selection improved using topological data analysis.

Unsupervised clustering can reproduce categorization systems if features and metrics are correctly selected.

This paper reviews feature selection methods using swarm intelligence.

In most gene expression data, the number of training samples is very small compared to the large number of genes involved in the experiments. However, among the large amount of genes, only a small fraction is effective for performing a certain task. Furthermore, a small subset of genes is desirable in developing gene e…

FoLDTree improves oblique decision trees with ULDA, enhancing accuracy and feature selection.

Guided adaptive shrinkage uses co-data to improve feature selection in genomic studies.

Paper proposes a new method to select memory data for online class-incremental learning.

We present a selective sampling method designed to accelerate the training of deep neural networks. To this end, we introduce a novel measurement, the minimal margin score (MMS), which measures the minimal amount of displacement an input should take until its predicted classification is switched. For multi-class linear…