Stacking is a general approach for combining multiple models toward greater predictive accuracy. It has found various application across different domains, ensuing from its meta-learning nature. Our understanding, nevertheless, on how and why stacking works remains intuitive and lacking in theoretical insight. In this …
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48 results for “stacking algorithm”
Study uses stacked generalization to improve fraud detection algorithms.
problem Improving performance of algorithms in imbalanced fraud data sets.
method Two-step process combining machine learning methods and cross-validation.
result Improved performance metrics on resampled fraud data sets.
StackGenVis simplifies ensemble learning by visualizing model selection and performance.
problem Complexity in choosing and combining models for stacking ensemble learning.
method Visual analytics system that dynamically adapts performance metrics, manages data instances, selects algorithms, and measures predictive performance.
result Reduces complexity of stacking by removing overpromising and underperforming models.
LESS combines local predictors for subsets to learn from heterogeneous input-output pairs.
problem Learning from heterogeneous input-output pairs in populations with varied behavior.
method LESS algorithm: generates subsets, trains local predictors, combines them.
result LESS is highly competitive compared to state-of-the-art methods.
Study evaluates different meta-learners for multi-view stacking.
problem Choosing the best meta-learner for multi-view stacking.
method Seven different meta-learners were evaluated in simulations and real data.
result Nonnegative lasso, nonnegative adaptive lasso, and nonnegative elastic net are suitable meta-learners.
Heart rate estimation from electrocardiogram signals is very important for the early detection of cardiovascular diseases. However, due to large individual differences and varying electrocardiogram signal quality, there does not exist a single reliable estimation algorithm that works well on all subjects. Every algorit…
Paper proves stacking ensembling is effective and proposes a new family of stacked generalizations.
problem Lack of theoretical guarantees for stacking ensembling methods.
method Proves novel theoretical result and proposes a new family of stacked generalizations.
result Proves stacking ensembling is effective and proposes a new family of stacked generalizations.
pystacked combines machine learning models for improved predictions.
problem Improving machine learning model performance through stacking.
method Stacked generalization using Python's scikit-learn with various base learners.
result Enhanced predictive models through combining multiple machine learning algorithms.
A method to improve gradient boosting models using stacking.
problem Improving the performance of gradient boosting models.
method Proposes a stacking algorithm to learn a meta-model for ensembles of gradient boosting models.
result The proposed approach can be extended to differentiable combination models like neural networks.
Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variability. Incorrect warfarin dosing is associated with devastating adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic va…
We review the basic definition of a stack and apply it to the topological and smooth settings. We then address two subtleties of the theory: the correct definition of a ``stack over a stack'' and the distinction between small stacks (which are algebraic objects) and large stacks (which are generalized spaces).
In this article, we derive many properties of étale stacks in various contexts, and prove that étale stacks may be characterized categorically as those stacks that arise as prolongations of stacks on a site of spaces and local homeomorphisms. Moreover, we show that the bicategory of étale differentiable stacks and loca…
Constructs cohomology decompositions for symmetric stacks.
problem Cohomology of symmetric stacks.
method Constructs decompositions of cohomology, Borel--Moore homology, and vanishing cycle cohomology.
result Defines BPS cohomology and proves its equivalence to intersection cohomology for smooth stacks.
Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predicting missing links, given a partially observed network, but it has re…
New insights into choosing between two data integration methods based on SVD.
problem Choosing between two data integration methods (Stack-SVD and SVD-Stack) for shared latent structure across multiple datasets.
method Derive exact expressions for the asymptotic performance and phase transitions of Stack-SVD and SVD-Stack, and develop optimal weighting schemes.
result Optimally weighted Stack-SVD outperforms optimally weighted SVD-Stack in the asymptotic regime.
New neural stack and Turing Machine architectures prove stability and computational power.
problem Designing stable neural network architectures for Turing Machine simulation.
method Introducing neural stack and Turing Machine architectures, proving stability and computational equivalence.
result Differentiable nnTM with bounded neurons can simulate Turing Machine in real-time and is equivalent to UTM.
Bayesian stacking improves model performance with varying model weights.
problem Improving model predictions with heterogeneous input performance.
method Bayesian hierarchical stacking with varying model weights inferred via Bayesian inference.
result Hierarchical stacking yields better predictions than linear averaging.
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriorsstat.ME
Stacking improves inference for multimodal Bayesian posterior distributions.
problem Difficulty of MCMC in moving between modes and underestimation of posterior uncertainty.
method Parallel runs of MCMC, variational, or mode-based inference, combined using Bayesian stacking.
result Stacking efficiently samples from multimodal posterior distributions and represents uncertainty better than variational inference.
The paper proposes a demand prediction model for e-commerce sites using machine learning and stacking.
problem Accurately predicting demand for products sold by multiple sellers at different prices.
method Applied different regression algorithms and stacked generalization for demand prediction.
result Stacked generalization produced almost as good results as individual machine learning methods.
Paper combines machine learning and model averaging for robust parameter estimation.
problem Estimating structural parameters with partially unknown functional forms.
method Pairing double/debiased machine learning with stacking for model averaging.
result DDML with stacking is more robust to unknown functional forms than single learners.
We introduce a stacking version of the Monte Carlo algorithm in the context of option pricing. Introduced recently for aeronautic computations, this simple technique, in the spirit of current machine learning ideas, learns control variates by approximating Monte Carlo draws with some specified function. We describe the…
We generalize the notion of a small sheaf of sets over a topological space or manifold to define the notion of a small stack of groupoids over an étale topological or differentiable stack. We then provide a construction analogous to the étalé space construction in this context, establishing an equivalence of 2-categori…
This paper demonstrates how to apply machine learning algorithms to distinguish good stocks from the bad stocks. To this end, we construct 244 technical and fundamental features to characterize each stock, and label stocks according to their ranking with respect to the return-to-volatility ratio. Algorithms ranging fro…
pRSL combines probabilistic rules to improve multi-label classification.
problem Modeling the structure between multi-label classes for better performance.
method Uses probabilistic propositional logic rules and belief propagation to combine predictions from multiple classifiers.
result pRSL achieves state-of-the-art performance on various benchmark datasets.
This work characterizes global quotient stacks---smooth stacks associated to a finite group acting a manifold---among smooth quotient stacks , where is a smooth manifold equipped with a smooth proper action by a Lie group . The characterization is described in terms of the action of the connected componen…
Survival Prediction of Heart Failure Patients using Stacked Ensemble Machine Learning Algorithmcs.LG
Study predicts heart failure patient survival using stacked ensemble ML.
problem Predicting survival of heart failure patients.
method Collect and analyze patient data, apply SMOTE, use K-Means, Fuzzy C-Means clustering, Random Forest, XGBoost, Decision Tree, and propose a stacked ensemble model.
result Supervised ML algorithms outperform unsupervised models, achieving high accuracy and F1 score.
The BPS decomposition theorem splits cohomology of symmetric stacks into invariant parts.
problem Decomposing the cohomology of smooth symmetric stacks into invariant parts.
method Using cohomological Hall induction and intersection cohomology of moduli spaces.
result Establishes the BPS decomposition theorem for various symplectic stacks.
Memory bandwidth bottleneck is a major challenges in processing machine learning (ML) algorithms. In-memory acceleration has potential to address this problem; however, it needs to address two challenges. First, in-memory accelerator should be general enough to support a large set of different ML algorithms. Second, it…
Stacked conformal predictionstat.ML
Stacked conformal prediction simplifies model validation.
problem Validating stacked predictive models efficiently.
method Meta-learner at the top of a stacked ensemble for approximate marginal validity.
result The method achieves approximate marginal validity without a separate calibration sample.
Constructs moduli stacks for quiver connections and extends non-Abelian Hodge theory.
problem Extending non-Abelian Hodge theory to moduli stacks of quiver connections.
method Formalizes and constructs moduli stacks of bundles with λ-connections over prestacks.
result Shows moduli stacks are algebraic and locally of finite presentation when base is smooth and projective.
Study optimizes shared singular subspace estimation from noisy matrices.
problem Estimating shared singular subspaces across multiple noisy matrices.
method Low-rank matrix denoising framework with Stack-SVD and novel estimators.
result Stack-SVD achieves minimax rate-optimality for identical shared subspaces, and novel estimators for partial sharing.
Study connections on Lie groupoids and stacks using Atiyah sequences.
problem No specific problem stated; general connections on Lie groupoids and stacks.
method Construct connections using Atiyah sequences associated with transversal tangential distributions.
result Detailed study and construction of connections on Lie groupoids and stacks.
In this paper, we consider diffeological spaces as stacks over the site of smooth manifolds, as well as the "underlying" diffeological space of any stack. More precisely, we consider diffeological spaces as so-called concrete sheaves and show that the Grothendieck construction sending these sheaves to stacks has a left…
Constructs moduli stacks of quiver bundles and applies to Higgs bundles.
problem Classifying morphisms of vector bundles over a fixed base.
method General method for constructing moduli stacks of diagrams of vector bundles indexed by a simplicial set.
result Recovery of Nakajima quiver varieties and alternate construction of moduli stacks of Higgs bundles.
Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectrastat.ML
Improved prediction of soil parameters using Multi-target Stacked Generalisation on EDXRF spectra.
problem Challenges in predicting multiple soil parameters accurately from EDXRF spectra.
method Multi-target Stacked Generalisation (MTSG) method combining multiple regression models.
result MTSG significantly improved prediction accuracy for multiple soil parameters, reducing average error from 0.67 to 0.64.
New potentials found for sheaves on Calabi-Yau 4-folds.
problem Understanding sheaves on Calabi-Yau 4-folds.
method Derived Quot-stacks and Lagrangian distributions.
result Globally defined -shifted potentials on sheaves.
We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain. Standard neural networks fail to a large extent on compositional learning. We propose Tree Stack Memory Units (Tree-SMU) to enable strong compositional generalization. Tree-SMU is a recurs…
Stacks in Poisson Geometrymath.DG
This thesis is divided into four chapters. The first chapter discusses the relationship between stacks on a site and groupoids internal to the site. It includes a rigorous proof of the folklore result that there is an equivalence between the bicategory of internal groupoids and the bicategory of geometric stacks. The s…
Stacked LSTM networks improve traffic volume forecasting.
problem Accurate traffic volume prediction for better planning.
method Applying stacked Long Short-Term Memory (LSTM) networks for time series forecasting.
result Stacked LSTM networks enhance the accuracy of traffic volume predictions.
Studies geometric structures on Lie groupoids and differentiable stacks.
problem None explicitly stated in the abstract.
method Various geometric structures and connections on Lie groupoids and differentiable stacks.
result Introduces new concepts like topological groupoid extensions and gerbes over topological stacks.
Stacking improves deep neural network training efficiency.
problem Improving the efficiency of training deep neural networks.
method Proposes stacking as a form of accelerated gradient descent.
result Proves stacking provides accelerated training for certain deep linear residual networks.
A theory of dg schemes is developed so that it becomes a homotopy site, and the corresponding infinity category of stacks is equivalent to the infinity category of stacks, as constructed by Toen and Vezzosi, on the site of dg algebras whose cohomologies have finitely many generators in each degree. Stacks represented b…
We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the …
Study moduli spaces of elliptic PDEs using derived -geometry.
problem Representability of moduli spaces of solutions of elliptic PDEs.
method Derived -geometry, stacks of relative jets, nonlinear Fredholm analysis.
result Moduli stack of solutions is relatively representable by quasi-smooth derived -schemes.
Stacking methods improve the prediction performance of regression models. A simple way to stack base regressions estimators is by combining them linearly, as done by \citet{breiman1996stacked}. Even though this approach is useful from an interpretative perspective, it often does not lead to high predictive power. We pr…
We develop a universal framework to study smooth higher orbifolds on the one hand and higher Deligne-Mumford stacks (as well as their derived and spectral variants) on the other, and use this framework to obtain a completely categorical description of which stacks arise as the functor of points of such objects. We choo…
For a compact manifold M and a differentiable stack \cX presented by a Lie groupoid X, we show the Hom-stack Hom(M,\cX) is presented by a Fréchet-Lie groupoid Map(M,X) and so is an infinite-dimensional differentiable stack. We further show that if \cX is an orbifold, presented by a proper étale Lie groupoid, then Map(M…
We propose a novel stacked generalization (stacking) method as a dynamic ensemble technique using a pool of heterogeneous classifiers for node label classification on networks. The proposed method assigns component models a set of functional coefficients, which can vary smoothly with certain topological features of a n…