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.
Bayesian QFSTS model tackles feature selection in quantile time series analysis.
Bayesian neural network improves feature selection and prediction.
Proposes a new method for feature selection using Bayesian ID with intervention.
It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest which features and feature interactions are relevant to the prediction task. We present a novel method, Selective Bayesian Forest Classifie…
Gaussian OBFS proves strong consistency in feature selection with correlations.
VFDS selects dynamic features for efficient HAR tasks, optimizing performance-cost trade-offs.
Bayesian principles improve neural additive models for better feature selection and uncertainty.
Paper proposes BTuD for unsupervised feature selection.
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…
Enhances FA framework for heterogeneous data with feature selection and semi-supervised learning.
In many real-world scenarios where data is high dimensional, test time acquisition of features is a non-trivial task due to costs associated with feature acquisition and evaluating feature value. The need for highly confident models with an extremely frugal acquisition of features can be addressed by allowing a feature…
Novel Bayesian model improves EEG-based BCI character selection.
Bayesian neural networks are compressed using feature and weight pruning based on posterior inclusion probabilities.
Automates model selection for GLMs using optimization.
Bayesian TNKMs automatically infer model complexity and feature relevance.
Bayesian neural networks explore rare fluctuations for better feature learning.
Identifying small subsets of features that are relevant for prediction and/or classification tasks is a central problem in machine learning and statistics. The feature selection task is especially important, and computationally difficult, for modern datasets where the number of features can be comparable to, or even ex…
We present a Bayesian method for feature selection in the presence of grouping information with sparsity on the between- and within group level. Instead of using a stochastic algorithm for parameter inference, we employ expectation propagation, which is a deterministic and fast algorithm. Available methods for feature …
FBMS R package simplifies Bayesian model selection and averaging.
There is no known efficient method for selecting k Gaussian features from n which achieve the lowest Bayesian classification error. We show an example of how greedy algorithms faced with this task are led to give results that are not optimal. This motivates us to propose a more robust approach. We present a Branch and …
Bayesian framework selects features and lags for time series forecasting.
Optimal Bayesian feature filtering (OBF) is a supervised screening method designed for biomarker discovery. In this article, we prove two major theoretical properties of OBF. First, optimal Bayesian feature selection under a general family of Bayesian models reduces to filtering if and only if the underlying Bayesian m…
Incorporating feature selection into a classification or regression method often carries a number of advantages. In this paper we formalize feature selection specifically from a discriminative perspective of improving classification/regression accuracy. The feature selection method is developed as an extension to the r…
Feature selection, identifying a subset of variables that are relevant for predicting a response, is an important and challenging component of many methods in statistics and machine learning. Feature selection is especially difficult and computationally intensive when the number of variables approaches or exceeds the n…
Feature selection is an important task in many problems occurring in pattern recognition, bioinformatics, machine learning and data mining applications. The feature selection approach enables us to reduce the computation burden and the falling accuracy effect of dealing with huge number of features in typical learning …
EBBS integrates expert assessments into MIO best-subsets problem.
In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that c arries out a greedy search through the space of features. We hyp…
BayesBoost combines boosting and Bayesian methods for linear mixed models, improving uncertainty estimation and variable selection.
Cost-effective feature selection improves network model choice.
Approximate Bayesian computation is an established and popular method for likelihood-free inference with applications in many disciplines. The effectiveness of the method depends critically on the availability of well performing summary statistics. Summary statistic selection relies heavily on domain knowledge and care…
In this paper, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature sel…
The paper evaluates company investment value using machine learning models.
Flexible model for complex relationships using Bayesian nonparametrics.
Bayesian method identifies causal sets across populations without graph knowledge.
PliableBVS extends Bayesian lasso for modeling interactions with modifying variables.
Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity…
SVB method provides scalable Bayesian proportional hazards model for high-dimensional gene expression data.
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…
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…
Paper improves feature selection accuracy using transfer learning.
Improves decision-making by correcting feature selection bias.
High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of different classes (e.g. cancer and normal) from tens of thousands of genes that are active (expressed) in certain tissue cells. To this end, we wi…
Bayesian Cox model identifies biomarkers from multi-omics data.
Bayesian-guided method selects optimal design from large candidate pool.
Proposes a new algorithm for Sparse Bayesian Learning connected to Stepwise Regression.
A gradient-based method learns the structure of TAN for Bayesian network classifiers.
Infinite BART model selects number of trees and allows different functions for clusters.
This work analyzes tree-based methods from a ranking perspective, providing insights and new statistics.