arXiv research

Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.

Proposes a Siamese NN for algorithm selection focusing on alike performing instances.

Improves algorithm selection for thousands of candidates using dyadic features.

A stability metric compares feature selection algorithms in machine learning.

In dictionary selection, several atoms are selected from finite candidates that successfully approximate given data points in the sparse representation. We propose a novel efficient greedy algorithm for dictionary selection. Not only does our algorithm work much faster than the known methods, but it can also handle mor…

Study on selecting between base algorithms in stochastic bandit problems.

Paper proposes an unsupervised feature selection algorithm with stability guarantees.

Unified view on selective credit assignment for reinforcement learning.

When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset \emph{and} the best set of hyperparameters for the chosen model. The usual approach is to apply a nested cross-validation procedure; hyperparameter selection is performed…

Run2Survive uses survival analysis for algorithm selection, outperforming traditional methods.

One of the classical problems in machine learning and data mining is feature selection. A feature selection algorithm is expected to be quick, and at the same time it should show high performance. MeLiF algorithm effectively solves this problem using ensembles of ranking filters. This article describes two different wa…

As known, attribute selection is a method that is used before the classification of data mining. In this study, a new data set has been created by using attributes expressing overall satisfaction in Turkey Statistical Institute (TSI) Life Satisfaction Survey dataset. Attributes are sorted by Ranking search method using…

Paper proposes a statistical test for feature selection pipelines using selective inference.

IWeS selects examples by entropy-based importance sampling for subset selection.

In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stability Selection was proposed that can provide reliable finite-sample control of the number of false positives. Its benefits were demonstrated …

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, the…

Fast feature selection for SHM using canonical correlation.

A new feature selection method using attention for neural networks.

New algorithm tackles self-selection bias in estimating linear regressors.

Top-two algorithm improved for best-k-arm selection.

We present a new Bitcoin coin selection algorithm, "coin selection with leverage", which aims to improve upon cost savings than that of standard knapsack like approaches. Parameters to the new algorithm are available to be tuned at the users discretion to address other goals of coin selection. Our approach naturally fi…

New algorithm selects variables from large datasets.

A fast algorithm selects best subsets in high-dimensional models.

Active feature selection uses mutual information to choose fewer labels for better feature selection.

In unsupervised learning, an unbiased uniform sampling strategy is typically used, in order that the learned features faithfully encode the statistical structure of the training data. In this work, we explore whether active example selection strategies - algorithms that select which examples to use, based on the curren…

Grover search for optimal portfolios based on Sharpe ratio.

The study explores whether model selection guarantees apply to contextual bandits.

NGP selects N features from P using neural networks in a greedy, iterative process.

Forward-backward selection is one of the most basic and commonly-used feature selection algorithms available. It is also general and conceptually applicable to many different types of data. In this paper, we propose a heuristic that significantly improves its running time, while preserving predictive accuracy. The idea…

A new framework selects information sources to test hypotheses robustly, even with misclassifications.

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

For massive data sets, efficient computation commonly relies on distributed algorithms that store and process subsets of the data on different machines, minimizing communication costs. Our focus is on regression and classification problems involving many features. A variety of distributed algorithms have been proposed …

New MCMC algorithm reduces subset selection passes to 2 for optimal $k$-dimensional subspace approximation.

New algorithm finds best subset in high-dimensional data models.

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…

A genetic algorithm with community detection improves feature selection accuracy.

A meta-learning approach for efficient algorithm selection in budget-limited scenarios.

A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure. In this work we propose a novel feature selection…

A new algorithm removes unexpected correlations in biased data for better clustering.

Develops ELBD for efficient feature selection in VAE latent variables.

As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed. Online streaming feature selection provides a new, complementary algorithmic methodology to enrich online feature selection, especially targets t…

New algorithm identifies causal relationships from graphs, even with selection bias.

New method estimates selection event for black-box models.

New algorithm offers costless model selection in contextual bandits.

OOMP selects features online for sparse linear regression.

Study compares single vs ensemble feature selection for cancer diagnosis.

Machine learning improves Buchberger's algorithm for polynomial systems.

We consider the problem of matrix column subset selection, which selects a subset of columns from an input matrix such that the input can be well approximated by the span of the selected columns. Column subset selection has been applied to numerous real-world data applications such as population genetics summarization,…