FSinR is a comprehensive R package for feature selection.
problem Feature selection in machine learning.
method Filter and wrapper methods, search algorithms.
result Provides a variety of feature selection methods and search algorithms.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
Trend · papers per month
48 results for “wrapper search”
We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining …
New method reduces errors in causal discovery from data.
problem Errors in causal discovery from limited data.
method Hierarchical wrapper for constraint-based algorithms.
result Significantly fewer tests, more accurate graphs, shorter run-times.
Enhanced framework predicts transportation modes from GPS data.
problem Predicting transportation modes from GPS records.
method Extended Etemad et al. framework, investigated wrapper search and information retrieval methods for feature selection, compared with deep learning methods.
result Framework achieved better performance than related papers.
A hybrid method combines GA and EN for feature selection in high-dimensional datasets.
problem Feature selection in high-dimensional datasets with high prediction error and computational inefficiency.
method Hybrid two-layer approach using Genetic Algorithm and Elastic Net.
result The hybrid method improves prediction accuracy and reduces computational time.
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic i…
A new tensor completion method using tensor networks with Tucker wrapper.
problem Low-rank tensor completion in various applications.
method Solving LRTC as a system of nonlinear equations using a two-level alternative least squares method.
result The method converges to the exact solution at a linear rate with high probability.
A deep learning subsampling technique improves modulation classification accuracy.
problem Improving modulation classification accuracy in wireless communication systems.
method Proposes a data-driven subsampling strategy using deep neural networks to simulate signal removal.
result Improves classification accuracy to higher levels than traditional methods.
SWAG combines screening and wrapper methods for interpretable sparse learning.
problem Need for interpretable and replicable machine learning results in sparse settings.
method Greedy exploration of attribute space to find sparse learners.
result Library of equivalent learners for attribute availability.
Powershap speeds up feature selection for machine learning models.
problem Feature selection in machine learning models is crucial but computationally expensive.
method Powershap combines statistical hypothesis testing and Shapley values for quick feature selection.
result Powershap outperforms other methods with similar predictive performance and significantly faster execution.
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditabilitycs.LG
Wrapper improves black-box model auditability and decision trustworthiness.
problem Lack of transparency and auditability in machine learning models used in complex applications.
method Integrates uncertainty measures into black-box models to enhance auditability and decision trustworthiness.
result Improves trust in machine learning models by providing actionable mechanisms to reject uncertain predictions.
A new feature selection method for semi-supervised learning with imperfect labels.
problem Feature selection for semi-supervised learning with imperfectly labeled data.
method Genetic algorithm for proposing feature subsets, probabilistic error model for mislabeling, multi-class C-bound selection criterion.
result Empirical results show the effectiveness of the proposed framework compared to state-of-the-art approaches.
Feature selection (FS) has become an indispensable task in dealing with today's highly complex pattern recognition problems with massive number of features. In this study, we propose a new wrapper approach for FS based on binary simultaneous perturbation stochastic approximation (BSPSA). This pseudo-gradient descent st…
Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new compu…
Genetic algorithms are a well-known method for tackling the problem of variable selection. As they are non-parametric and can use a large variety of fitness functions, they are well-suited as a variable selection wrapper that can be applied to many different models. In almost all cases, the chromosome formulation used …
New method uses MCTS only at test time for faster game learning.
problem High computational demands in game learning.
method Combines MCTS with TD learning agents for faster, reproducible agents.
result First learning-from-scratch agent to beat Edax up to level 7.
When Should an AI Workflow Release? Always-Valid Inference for Black-Box Generate-Verify Systemsstat.ML
A new method for releasing AI workflows to avoid premature incorrect results.
problem Statistical challenges in releasing AI workflows with adaptive scoring.
method Wrapper that calibrates and accumulates evidence from high-scoring failures.
result Reduces premature incorrect release while still releasing on moderate evidence.
Recently we proposed a general, ensemble-based feature engineering wrapper (FEW) that was paired with a number of machine learning methods to solve regression problems. Here, we adapt FEW for supervised classification and perform a thorough analysis of fitness and survival methods within this framework. Our tests demon…
ED-Filter improves eating disorder classification on Twitter.
problem High dimensionality and extensive feature sets in Twitter data for ED classification.
method Informed branch and bound search technique with hybrid greedy-based deep learning.
result Significant improvements in classification accuracy and efficiency.
Paper shows how to quantify uncertainty in medical ML models.
problem Uncertainty in opaque ML models can lead to safety risks in medical applications.
method Introduces Uncertainty Wrapper to quantify uncertainty transparently.
result Demonstrates practical utility of Uncertainty Wrapper in flow cytometry.
Tool converts industrial systems to RL environments for optimization.
problem Lack of RL methods for optimizing industrial systems.
method Develops a tool to convert FSM models to OpenAI Gym wrappers.
result Traditional RL methods can be applied to industrial systems.
Quantitative structure-activity relationship (QSAR) modelling is effective 'bridge' to search the reliable relationship related bioactivity to molecular structure. A QSAR classification model contains a lager number of redundant, noisy and irrelevant descriptors. To address this problem, various of methods have been pr…
This research optimizes feature selection for predicting transportation modes in smart cities.
problem Finding the best subset of features for predicting transportation modes.
method Wrapper and information retrieval methods were used to find the best feature subset.
result The proposed framework achieved better performance compared to related studies.
CSA fills a gap in RLVR-trained LLM deployment by providing anytime-valid selective risk control.
problem Deployment of RLVR-trained LLMs in regulated organizations requires a safety certificate for every round without waiting for long-run averages.
method CSA uses a (test statistic, validity guarantee, deployment rule) framework to fill the gap, maintaining a Ville-type e-process per threshold on a Bonferroni grid.
result CSA provides the first anytime-valid selective risk control for RLVR-trained LLMs, matching the long-run average certification rate and satisfying pathwise validity and non-refusing deployment on every cell.
AMBER method selects features efficiently using autoencoders and model-based elimination.
problem Efficiently selecting relevant features for classification.
method Greedy backward elimination using a ranker model and autoencoders.
result AMBER outperforms other feature selection methods in classification accuracy.
MDP Playground tests RL agents across various dimensions for better understanding and debugging.
problem Understanding and debugging reinforcement learning agents across diverse environments and dimensions.
method Controlled testbed with adjustable dimensions for different RL challenges.
result Insights into agent performance and interaction with various dimensions.
The study explores using unlabeled data to improve survival time predictions.
problem Challenges in clinical follow-up studies due to drop-out and data collection issues.
method Investigates three approaches to incorporate unlabeled data in survival analysis.
result All approaches improve predictive performance over independent test data.
This paper proposes an improved active learning method using classification trees.
problem Reducing the size of training sets while maintaining high accuracy in supervised learning.
method A wrapper active learning method using a classification tree to sub-sample from low-entropy regions.
result The proposed method constructs accurate classification models even with severely restricted labeled data.
New risk class defined based on loss location and deviation.
problem Risk assessment in loss distributions.
method Wrapper around smooth loss functions, M-estimators, stochastic gradient methods.
result Finite-sample stationarity guarantees for stochastic gradient methods.
The paper proposes a method to automatically determine the number of clusters in data.
problem Determining the number of clusters in data without making strong assumptions.
method Using nonparametric concentration inequalities to infer the number of clusters.
result The method can automatically determine the appropriate value for k, without assumptions on the data distribution.
Unsupervised learning with GLRM feature selection reveals novel traumatic brain injury phenotypescs.LG
Unsupervised clustering reveals novel TBI phenotypes.
problem Inadequate categorization of traumatic brain injury (TBI) based on symptoms.
method Applied unsupervised learning with GLRM feature selection.
result Identified four novel TBI phenotypes with distinct feature profiles.
SPSA-FSR: Simultaneous Perturbation Stochastic Approximation for Feature Selection and Rankingstat.ML
This manuscript presents the following: (1) an improved version of the Binary Simultaneous Perturbation Stochastic Approximation (SPSA) Method for feature selection in machine learning (Aksakalli and Malekipirbazari, Pattern Recognition Letters, Vol. 75, 2016) based on non-monotone iteration gains computed via the Barz…
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter . Suppose the co- variance graph formed by thresholding the entries of the sample covariance matrix at is decomposed into connected components. We show that the vertex-partition induced by the thresh…
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regular and sparse online learning algorithms for large-scale binary and multi-class classification tasks with high efficiency, scalability, porta…
In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text categorization. Unlike most existing feature selection approaches, the proposed FSMJ approach is based on real-valued features which provide mo…
Picasso is a new library for sparse learning problems in R and Python.
problem Sparse learning problems in high-dimensional data analysis.
method Unified framework of pathwise coordinate optimization with efficient active set selection strategies.
result picasso can efficiently handle large-scale problems.
Algorithm learns fair division from noisy feedback in uncertain markets.
problem Learning fair division in uncertain markets with noisy feedback.
method Wrapper algorithms using dual averaging to learn item and agent values from bandit feedback.
result Asymptotically achieves optimal Nash social welfare in linear Fisher markets.
The paper reviews exchangeability and its implications for conformal prediction and rank tests.
problem Ensuring distribution-free predictive inference in machine learning and statistics.
method Explains exchangeability and its role in conformal prediction and rank tests.
result Discovers similarities between conformal prediction and rank tests based on exchangeability.
Feature selection is frequently used as a pre-processing step to machine learning. It is a process of choosing a subset of original features so that the feature space is optimally reduced according to a certain evaluation criterion. The central objective of this paper is to reduce the dimension of the data by finding a…
In this paper, we are interested in constructing general graph-based regularizers for multiple kernel learning (MKL) given a structure which is used to describe the way of combining basis kernels. Such structures are represented by sum-product networks (SPNs) in our method. Accordingly we propose a new convex regulariz…
MH-GAN uses a discriminator to improve sampling from a GAN's distribution.
problem Improving sampling from a GAN's implicitly defined distribution.
method Combines Markov chain Monte Carlo and GANs, using a discriminator to wrap the generator.
result MH-GAN samples from the true distribution even when the generator is imperfect.
A new language for neural architecture search decouples search spaces and algorithms.
problem Current neural architecture search methods are limited to specific use-cases and lack general-purpose constructs.
method Proposes a formal language for encoding search spaces over general computational graphs, allowing modular, composable, and reusable encodings.
result The language enables easy experimentation with different search spaces and algorithms without reinventing the wheel.
This paper compares Grid Search, Random Search, and Genetic Algorithm for NAS.
problem Hyperparameter optimization for neural architecture search.
method Comparison of Grid Search, Random Search, and Genetic Algorithm.
result Genetic Algorithm outperforms Grid Search and Random Search in terms of accuracy and execution time.
Efficient search methods can outperform random search on challenging tasks.
problem Comparing the performance of efficient and random search methods in neural architecture search.
method Comparison of weight sharing and random search methods on progressively larger search spaces for image classification and detection.
result Efficient search methods can provide substantial gains over random search on large, realistic tasks.
A new framework generates large hierarchical search spaces for neural architectures.
problem Discovering neural architectures from simple blocks is hard.
method Context-free grammars for a unified, scalable search space.
result Efficiently searches over complete architectures, outperforming existing methods.
MICO uses mutual information co-training to improve selective search efficiency.
problem Efficiently search and route unseen queries in large-scale search systems.
method Mutual Information Co-training framework for selective search with minimal supervision.
result Significantly improves performance on multiple metrics of selective search.
This work recommends personalized search stories to users based on their interests.
problem Personalized search story recommendation within search engines.
method Deep reinforcement learning architecture trained by imitation learning and reinforcement learning.
result Empirically demonstrated effectiveness on real-world data sets.
A method to reduce memory usage in NAS by pruning the search space.
problem High GPU memory consumption in One-Shot NAS techniques.
method Utilising Zero-Shot NAS to prune the search space before applying One-Shot NAS.
result Reduces memory consumption by 81% while maintaining accuracy.