arXiv research

This paper improves volatility forecasting using dynamic subset selection in genetic programming.

A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively splits the set of classes into two subsets, and trains a binary classifier to distinguish between each subset. Even though ensembles of nested dichotomies with random structure…

Random small feature subsets outperform FS in diverse datasets.

A new algorithm FS-PeSOA extracts features from large datasets.

Paper reinterprets ARP algorithm and improves its analysis and speed.

A new method selects columns of a matrix for dimensionality reduction with better error bounds and performance.

Given a Gaussian Markov random field, we consider the problem of selecting a subset of variables to observe which minimizes the total expected squared prediction error of the unobserved variables. We first show that finding an exact solution is NP-hard even for a restricted class of Gaussian Markov random fields, calle…

Proposes a few-shot learning method for feature selection without labeled data.

BWS selects best window subsets for efficient data pruning.

We introduce extensions of stability selection, a method to stabilise variable selection methods introduced by Meinshausen and Bühlmann (J R Stat Soc 72:417-473, 2010). We propose to apply a base selection method repeatedly to random observation subsamples and covariate subsets under scrutiny, and to select covariates …

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of ele…

The paper introduces a framework to select efficient datasets for preserving model rankings.

Improves predictive performance of nested dichotomies.

New method selects sparse predictors in large LMMs.

Unified methods for fast column selection in various applications.

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,…

A/B testing improves marketing decisions by selecting effective stratification variables.

This paper deals with prediction of anopheles number, the main vector of malaria risk, using environmental and climate variables. The variables selection is based on an automatic machine learning method using regression trees, and random forests combined with stratified two levels cross validation. The minimum threshol…

Consistent selection of predictors in high-dimensional binary models with misspecified parameters.

Subset selection in multiple linear regression aims to choose a subset of candidate explanatory variables that tradeoff fitting error (explanatory power) and model complexity (number of variables selected). We build mathematical programming models for regression subset selection based on mean square and absolute errors…

Bayesian method selects interacting regions in Markov models.

In this paper we propose strategies for estimating performance of a classifier when labels cannot be obtained for the whole test set. The number of test instances which can be labeled is very small compared to the whole test data size. The goal then is to obtain a precise estimate of classifier performance using as lit…

Microarray cancer gene expression data comprise of very high dimensions. Reducing the dimensions helps in improving the overall analysis and classification performance. We propose two hybrid techniques, Biogeography - based Optimization - Random Forests (BBO - RF) and BBO - SVM (Support Vector Machines) with gene ranki…

Selective removal of data subsets can efficiently unlearn unwanted distributions.

A determinantal point process (DPP) is a random process useful for modeling the combinatorial problem of subset selection. In particular, DPPs encourage a random subset Y to contain a diverse set of items selected from a base set Y. For example, we might use a DPP to display a set of news headlines that are relevant to…

The study improves the perceptron's storage capacity by optimizing variable selection.

Simplifies random forests by breaking down trees into rules for better interpretability.

This paper optimizes survey questions to reduce bias and select key tokens for QoE analysis.

Proposes an ensemble framework for outlier detection with embedded feature selection.

Paper proposes a method to identify negative transfers in multitask learning using surrogate models.

MOSAIC selects few informative exemplars from high-dimensional data with non-linear structures.

This paper deals with a new filter algorithm for selecting the smallest subset of features carrying all the information content of a data set (i.e. for removing redundant features). It is an advanced version of the fractal dimension reduction technique, and it relies on the recently introduced Morisita estimator of Int…

A new method for sparse linear bandits reduces exploration-exploitation tradeoff.

One-pass algorithm finds small subset for $\ell_p$ subspace approximation with additive error.

We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest. This problem can be viewed in the context of both feature selection and sparse approximation. We analyze the performance of widely used greedy heuristics, usi…

Bayesian approach selects subsets of variables for interpretable prediction and identifies key factors in educational outcomes.

Efficiently predict LLM benchmarks using feature selection and regression.

The observation of power laws in the time to extrema of volatility, volume and intertrade times, from milliseconds to years, are shown to result straightforwardly from the selection of biased statistical subsets of realizations in otherwise featureless processes such as random walks. The bias stems from the selection o…

We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation. The observed process samples are assumed uncorrelated over time and having a time-varying marginal dist…

Simplifies large action space bandits by selecting representative actions.

We propose a tree regularization framework, which enables many tree models to perform feature selection efficiently. The key idea of the regularization framework is to penalize selecting a new feature for splitting when its gain (e.g. information gain) is similar to the features used in previous splits. The regularizat…

Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks,…

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

Optimizes kernel discrepancies by selecting subsets efficiently.

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

Hierarchical randomized smoothing improves model robustness for complex data.

Bayesian method selects subsets for LMMs with structured dependence.

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