Forward regression is a statistical model selection and estimation procedure which inductively selects covariates that add predictive power into a working statistical regression model. Once a model is selected, unknown regression parameters are estimated by least squares. This paper analyzes forward regression in high-…
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48 results for “forward selection”
Improving the Predictive Performances of Nearest Neighbors Learning by Efficient Variable Selectionstat.ML
Paper improves -NN predictive performance with efficient variable selection.
problem Improving predictive performance of -NN models.
method Efficient forward selection of predictor variables.
result Novel approach approaches outperformance of stepwise selection models.
Beam search improves feature selection for better model performance.
problem Improving feature selection for better model performance.
method Proposed beam search as a generalization of forward selection for feature selection.
result Beam search can outperform forward selection, especially with correlated features.
This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration. Selective-Backprop uses the output of a training example's forward pass to decide whether to use that example to compute gradients and update para…
We propose a neural architecture search (NAS) algorithm, Petridish, to iteratively add shortcut connections to existing network layers. The added shortcut connections effectively perform gradient boosting on the augmented layers. The proposed algorithm is motivated by the feature selection algorithm forward stage-wise …
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraintstat.ML
We consider forward-backward greedy algorithms for solving sparse feature selection problems with general convex smooth functions. A state-of-the-art greedy method, the Forward-Backward greedy algorithm (FoBa-obj) requires to solve a large number of optimization problems, thus it is not scalable for large-size problems…
Novel bounds for logistic regression coreset construction and feature selection.
problem Efficiently summarize and reduce logistic regression inputs.
method Feature space sketching for logistic regression.
result Tight bounds for coreset construction and feature selection.
Develops a forward variable selection method for interpretable random forest models.
problem Interpreting high-dimensional non-parametric models like random forests.
method Forward variable selection using CRPS as loss function, with hypothesis testing at each step.
result Method selects a smaller set of variables that optimizes predictive performance.
We present a method of variable selection for the sparse generalized additive model. The method doesn't assume any specific functional form, and can select from a large number of candidates. It takes the form of incremental forward stagewise regression. Given no functional form is assumed, we devised an approach termed…
In this era of big data, feature selection techniques, which have long been proven to simplify the model, makes the model more comprehensible, speed up the process of learning, have become more and more important. Among many developed methods, forward and stepwise feature selection regression remained widely used due t…
Bayesian method corrects for model selection multiplicity in regression.
problem Model selection multiplicity in regression analysis.
method Developed a Bayesian prior distribution based on Holm procedure analogy.
result Adequate multiplicity correction requires sparsity not provided by recommended priors.
Study of portfolio management under relative performance concerns using mean field games.
problem Portfolio management problems under relative performance concerns.
method Forward utilities of CARA type, mean field games, best response and equilibrium strategies.
result Solve forward-utility finite player game and mean-field game under asset specialization.
Adaptive Group Lasso selects important features in neural networks.
problem Lack of interpretability in neural networks.
method Adaptive Group Lasso for feature selection.
result Consistent feature selection for neural networks with theoretical guarantee.
Combines historical and market data for better portfolio selection.
problem Improving portfolio selection through diverse information integration.
method Bayesian learning via Gaussian mixture model to harmonize historical and market data.
result The method enhances forecasting accuracy and robustness across various capital markets.
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…
Machine learning predicts Bitcoin returns but trading performance drops with costs.
problem Trading Bitcoin predictions with transaction costs.
method XGBoost, LSTM, iTransformer models evaluated in walk-forward protocol; cost-aware execution filter implemented.
result Cost-aware execution filter restores profitability; XGBoost strategy outperforms.
This paper reviews SDR methods for multivariate response regression.
problem Handling sufficient dimension reduction for multivariate response regression.
method Characterizes SDR estimators as inverse or forward regression methods.
result Pooled marginal, projective resampling, distance-based, ordinary least squares, partial least squares, and semiparametric SDR estimators are discussed.
Selective classification improves trading strategies by abstaining from predictions.
problem Designing effective trading strategies using selective classification.
method Extends binary or multi-class classifiers to allow abstaining from predictions, evaluates across different feature sets and classifiers.
result Selective classifiers can improve trading performance by avoiding poor predictions.
A new method ranks and selects features without model fitting.
problem Feature importance measures algorithm-specific and need improvement.
method Integrates global sensitivity analysis with forward selection and backward elimination.
result Demonstrates clear advantage over state-of-the-art methods.
This paper addresses the problem of neighborhood selection for Gaussian graphical models. We present two heuristic algorithms: a forward-backward greedy algorithm for general Gaussian graphical models based on mutual information test, and a threshold-based algorithm for walk summable Gaussian graphical models. Both alg…
Greedy selection finds smaller, more accurate subnetworks.
problem Finding smaller, accurate subnetworks in large neural networks.
method Greedy forward selection starting from an empty network.
result Theoretical guarantee of finding subnetworks with lower loss.
An AI approach selects variables in linear models.
problem Selecting significant variables in linear regression models.
method Artificial Neural Network trained to determine variable significance based on OLS estimates.
result The AI approach outperforms traditional methods in accuracy and variable selection.
We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward deep neural network that allows selective execution. Given an input, only a subset of D2NN neurons are executed, and the particular subset is determined by the D2NN itself. By pruning unnecessary computation depending on input, D2NNs provide a…
Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that infer capacity-regularized network architectures from the data and require neither cross-validation nor fine-tuning when training the model. One of the two essential components of a PB…
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
problem Improving stock price prediction accuracy for Apple Inc. using technical indicators.
method Evaluation of 123 technical indicators and 10 regression models on 13 years of Apple Inc. data.
result Combining feature selection with regression models significantly improves prediction accuracy.
This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high-dimensional models? In particular, we look at the error rates and power of some multi-stage regression methods. In the first stage we fit a set of candidate models. In the second stage we s…
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…
The goal of query performance prediction (QPP) is to automatically estimate the effectiveness of a search result for any given query, without relevance judgements. Post-retrieval features have been shown to be more effective for this task while being more expensive to compute than pre-retrieval features. Combining mult…
Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various classes of methods, forward feature selection methods based on …
We consider the problem of optimal portfolio selection under forward investment performance criteria in an incomplete market. The dynamics of the prices of the traded assets depend on a pair of stochastic factors, namely, a slow factor (e.g. a macroeconomic indicator) and a fast factor (e.g. stochastic volatility). We …
We consider the problem of optimal portfolio selection under forward investment performance criteria in an incomplete market. Given multiple traded assets, the prices of which depend on multiple observable stochastic factors, we construct a large class of forward performance processes with power-utility initial data, a…
New framework improves classification accuracy using Pillai's trace and ULDA.
problem Traditional LDA's limitations in noise sensitivity and non-invertible matrices.
method Integrates Pillai's trace with ULDA for a unified classifier.
result Effective control of Type I error rates and improved classification accuracy.
Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial rati…
New method selects optimal bandwidth for price return density estimation, impacting efficient market hypothesis evaluation.
problem Estimating the complexity of price return distributions using kernel density estimation.
method Proposes a new complexity measure to select optimal bandwidth, avoiding overfitting and underfitting.
result Optimal bandwidth selection leads to clearer evaluation of the efficient market hypothesis.
Framework for lifelong learning using eigentasks to avoid forgetting and transfer knowledge.
problem Avoiding forgetting and transferring knowledge in lifelong learning.
method Eigentask framework: skills paired with generative models, wake-sleep cycle for learning and consolidation.
result Improved performance in supervised continual learning, evidence of forward knowledge transfer.
Feature selection methods are usually evaluated by wrapping specific classifiers and datasets in the evaluation process, resulting very often in unfair comparisons between methods. In this work, we develop a theoretical framework that allows obtaining the true feature ordering of two-dimensional sequential forward feat…
We combine forward investment performance processes and ambiguity averse portfolio selection. We introduce the notion of robust forward criteria which addresses the issues of ambiguity in model specification and in preferences and investment horizon specification. It describes the evolution of time-consistent ambiguity…
Study optimal portfolios for many players in a market model with random coefficients.
problem Optimal portfolio selection for many players under relative performance criteria in a market model with random coefficients.
method Game theory and stochastic optimal control, focusing on CARA and CRRA risk preferences, and extending to continuum of players.
result Existence of forward Nash equilibrium and mean field equilibrium for the n-agent game and corresponding mean field stochastic optimal control problem.
A new method selects variables efficiently for fast and accurate dynamic system identification.
problem Efficiently selecting variables for scalable Gaussian processes.
method Forward variable selection using Karhunen-Loève decomposition and Gibbs sampling.
result Method yields competitive accuracies and inference times for dynamic systems.
Develops a more powerful selective inference method for stepwise feature selection.
problem Loss of power in existing conditional SI methods due to over-conditioning.
method Uses homotopy continuation approach to overcome over-conditioning.
result Shows improved power and efficiency in selective inference for feature selection.
Investigates time-inconsistent portfolio selection under MMV preferences.
problem Time-inconsistent optimal strategies for MMV preferences.
method Nash equilibrium controls for MMV and MV preferences, solving FBSDE and HJB equations.
result MMV optimal strategies lead to higher investment amounts than MV strategies, narrowing over time.
New method proves neural networks can select features consistently.
problem Feature selection for deep neural networks is challenging.
method Adaptive Group Lasso selection procedure with Group Lasso as the base estimator.
result Adaptive Group Lasso is selection-consistent for a wide class of neural networks.
Dropout has proven to be an effective technique for regularization and preventing the co-adaptation of neurons in deep neural networks (DNN). It randomly drops units with a probability during the training stage of DNN. Dropout also provides a way of approximately combining exponentially many different neural networ…
Audit financial machine learning workflows to detect spurious predictability.
problem Spurious predictability in financial machine learning models.
method Falsification audit testing predictive workflows against synthetic environments.
result Many apparent financial predictions are artifacts, not genuine.
Scalable feature selection improves GBDT model training speed.
problem Efficiently selecting features for GBDT models in high-dimensional settings.
method Developed a scalable forward feature selection method using group testing.
result Significant speedups in training time with competitive model performance.
A new feature selection method using attention for neural networks.
problem Feature selection for neural networks with a budget constraint.
method Sequential Attention: greedy forward selection with attention weights.
result Achieves state-of-the-art results for neural networks.
Flexible selective inference using flow-based transport maps.
problem Selective inference with complex selection events.
method Flow-based generative modeling for conditional distribution approximation.
result Valid p-values and confidence sets for adaptively selected hypotheses and parameters.
Deep Neural Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory is physically finite, which limits the image resolutions and batch sizes that could be used in training for better DNN performance. Unlike solutions that require physically upgrade GPUs, the G…