arXiv research

AMBER method selects features efficiently using autoencoders and model-based elimination.

Two new feature selection algorithms improve on RFE.

We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features.We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions.We present four cas…

NodeDrop reduces network size by eliminating unnecessary features.

The paper introduces a method to eliminate latent discrimination in predictive models.

Value selection reduces model size while maintaining accuracy.

Margin maximization in the hard-margin sense, proposed as feature elimination criterion by the MFE-LO method, is combined here with data radius utilization to further aim to lower generalization error, as several published bounds and bound-related formulations pertaining to lowering misclassification risk (or error) pe…

A new feature selection method using random forest and Kolmogorov filter.

We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is specially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from $\el…

This research trains a supervised model to accurately detect PDF headings.

New methods reduce extrapolation errors in feature importance.

Two new Hie-TAN and Hie-TAN-Lite algorithms improve TAN for hierarchical feature spaces.

Sparse PCA provides a linear combination of small number of features that maximizes variance across data. Although Sparse PCA has apparent advantages compared to PCA, such as better interpretability, it is generally thought to be computationally much more expensive. In this paper, we demonstrate the surprising fact tha…

Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarit…

A new method ranks and selects features without model fitting.

Safe-DRFS selects features robust to covariate shifts for reliable performance.

New research shows spurious samples in deep models are not errors but a feature.

Fairness in Naive Bayes classifiers by identifying and eliminating discrimination patterns.

A new method reduces feature screening cost from $O(np)$ to $O(\sqrt{n}p)$.

A new variable importance measure for DRFs detects broader impacts on output distributions.

Optimal Coordinate Ascent (OCA) improves feature selection in machine learning.

Feature selection has attracted significant attention in data mining and machine learning in the past decades. Many existing feature selection methods eliminate redundancy by measuring pairwise inter-correlation of features, whereas the complementariness of features and higher inter-correlation among more than two feat…

Neural eliminators reduce unreliable classification by eliminating improbable classes.

Shapley value improves model interpretation but not causal inference.

Proposes a flexible tournament design combining knockout and round-robin.

We introduce a novel algorithm that computes the $k$-sparse principal component of a positive semidefinite matrix $A$. Our algorithm is combinatorial and operates by examining a discrete set of special vectors lying in a low-dimensional eigen-subspace of $A$. We obtain provable approximation guarantees that depend on t…

A new feature selection method for semi-supervised learning with imperfect labels.

Enhances detection of adverse drug events using diverse healthcare record data.

Improved elimination strategies for adaptive bandit identification reduce sample complexity and computational burden.

Regularization methods can overregulate, suppressing causal features.

Improves early stopping in deep networks by adjusting stepsizes.

AE-DQN learns to eliminate sub-optimal actions in complex RL environments.

A novel approach ODAR detects outliers for clustering.

New feature mapping approach improves recommendation accuracy and explainability.

Proposes a two-stage sector rotation method using machine learning and deep learning.

Safe screening rules reduce $\ell_0$-regression computation by fixing 76% of variables.

A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros. However, regularization often shrinks the estimator for relevant features, which leads to incorrect feature selection. Motivated by the above-mentioned issue…

Fast classification for sparse models, even with correlated features.

Feature selection can facilitate the learning of mixtures of discrete random variables as they arise, e.g. in crowdsourcing tasks. Intuitively, not all workers are equally reliable but, if the less reliable ones could be eliminated, then learning should be more robust. By analogy with Gaussian mixture models, we seek a…

Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose a subset of relevant samples from the input data and make reliable probabilistic predictions. However, in the presence of high-dimensional data with irrelevant features, traditional sparse Bayesian classifiers suffer from perfo…

We present algorithms for the detection of a class of heart arrhythmias with the goal of eventual adoption by practicing cardiologists. In clinical practice, detection is based on a small number of meaningful features extracted from the heartbeat cycle. However, techniques proposed in the literature use high dimensiona…

The paper compares LOCO and Shapley values for feature importance, highlighting their limitations and suggesting improvements.

Scalable feature selection improves GBDT model training speed.

New algorithm eliminates arms to minimize regret in complex bandit problems.

With the advent of Big Data era, data reduction methods are highly demanded given its ability to simplify huge data, and ease complex learning processes. Concretely, algorithms that are able to filter relevant dimensions from a set of millions are of huge importance. Although effective, these techniques suffer from the…

Tensor variable elimination for plated factor graphs enables exact inference in models with repeated structure.

This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction meth…

New feature selection methods reduce bias amplification in models.