Evaluating Meta-Learners for Multi-View Stacking: Balancing Classification Accuracy and View Selection

Kernekoncepter

The choice of meta-learner in multi-view stacking can significantly impact the trade-off between classification accuracy and view selection performance.

Resumé

The article investigates the performance of seven different meta-learners in the context of multi-view stacking (MVS), a framework for combining information from different feature sets (views) to build accurate classification models. The authors compare the meta-learners in terms of classification accuracy, true positive rate (TPR), false positive rate (FPR), and false discovery rate (FDR) in view selection, using both simulations and two real gene expression data sets.
The key findings are:

The nonnegative lasso, adaptive lasso, elastic net, and nonnegative forward selection (NNFS) generally showed comparable classification performance, while nonnegative ridge regression, stability selection, and the interpolating predictor performed noticeably worse in some conditions.

Among the well-performing meta-learners, model sparsity was associated with a lower FPR but also a lower TPR in view selection. However, there were situations where the sparser meta-learners obtained both a low FPR and a high TPR, particularly when the features from different views were uncorrelated.

Even when the FPR was very low, the FDR was often high, especially with a small sample size (n=200).

In the real data applications, the nonnegative lasso, adaptive lasso, elastic net, and NNFS selected a similar number of views, but the stability of the selected views varied, with the nonnegative lasso and adaptive lasso being the most stable.

The authors conclude that if both view selection and classification accuracy are important, the nonnegative lasso, adaptive lasso, and elastic net are suitable meta-learners, with the choice depending on the specific research context.

Statistik

"The sample size is either n=200 or n=2000."
"There are either V=30 or V=300 views, with each view containing either mv=250 or mv=2500 features."
"The population correlation between features from the same view is ρw=0.1, 0.5, or 0.9, and the population correlation between features from different views is ρb=0, 0.4, or 0.8."

Citater

"If both view selection and classification accuracy are important to the research at hand, then the nonnegative lasso, nonnegative adaptive lasso and nonnegative elastic net are suitable meta-learners."
"Exactly which among these three is to be preferred depends on the research context."
"The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three."

Vigtigste indsigter udtrukket fra

View selection in multi-view stacking: Choosing the meta-learner

by Wouter van L... kl. arxiv.org 04-16-2024

https://arxiv.org/pdf/2010.16271.pdf

Dybere Forespørgsler

What other meta-learner algorithms could be explored in the context of multi-view stacking

In the context of multi-view stacking, several other meta-learner algorithms could be explored to enhance view selection and classification performance. Some potential meta-learners to consider include:

Nonnegative Forward Stagewise Regression: This algorithm sequentially adds features to the model based on their individual contribution to improving prediction accuracy, while maintaining nonnegativity constraints on the coefficients.

Nonnegative Principal Component Analysis (PCA): By applying PCA with nonnegativity constraints, the meta-learner can identify the most important views by capturing the maximum variance in the data while ensuring interpretability.

Nonnegative Matrix Factorization (NMF): NMF can be used as a meta-learner to decompose the multi-view data into nonnegative components, aiding in feature selection and view interpretation.

Nonnegative Canonical Correlation Analysis (CCA): CCA with nonnegativity constraints can reveal relationships between views and the outcome, leading to improved view selection and classification accuracy.

Nonnegative Independent Component Analysis (ICA): ICA can extract independent sources of information from the multi-view data, potentially enhancing the meta-learning process for view selection.

Exploring these alternative meta-learner algorithms could provide valuable insights into the multi-view stacking framework and offer additional options for optimizing view selection and classification performance.

How would the performance of the meta-learners change if the underlying data structure or the relationship between features and the outcome were different

The performance of the meta-learners in multi-view stacking would vary based on changes in the underlying data structure or the relationship between features and the outcome. Here are some scenarios where the meta-learners' performance might be affected:

Highly Correlated Features: If the features within views are highly correlated, algorithms like nonnegative ridge regression or elastic net may perform better by handling multicollinearity effectively.

Sparse Data: In cases where the data is sparse with few informative features, the lasso or adaptive lasso may outperform other meta-learners by promoting sparsity in the model.

Nonlinear Relationships: If the relationship between features and the outcome is nonlinear, algorithms like nonnegative adaptive lasso or nonnegative forward selection, which can capture complex relationships, may yield better results.

Imbalanced Data: For imbalanced datasets, stability selection combined with nonnegative lasso could help in selecting relevant views while controlling false discoveries.

Noise in Data: When there is noise in the data, the interpolating predictor may struggle due to overfitting, while nonnegative ridge regression could provide more robust results.

Adapting the choice of meta-learner based on the specific characteristics of the data can lead to improved performance in multi-view stacking tasks.

How can the stability of the selected views be further improved, especially when the sample size is small, without sacrificing too much predictive accuracy

To enhance the stability of selected views in multi-view stacking, especially with a small sample size, without compromising predictive accuracy, several strategies can be employed:

Bootstrapping: Utilize bootstrapping techniques to create multiple samples from the original data and perform view selection on each bootstrap sample. The consensus of selected views across multiple samples can improve stability.

Ensemble Methods: Combine the results of multiple meta-learners or multiple runs of the same meta-learner to reduce variability in view selection and enhance stability.

Regularization Strength: Adjust the regularization strength of the meta-learner to control the complexity of the model and prevent overfitting, thereby improving stability.

Feature Importance Ranking: Rank the selected views based on their importance scores or coefficients to identify the most stable and relevant views for the prediction task.

Cross-Validation: Implement nested cross-validation to evaluate the stability of selected views across different folds and ensure the robustness of the model.

By incorporating these strategies, the stability of selected views in multi-view stacking can be enhanced, even in scenarios with limited sample sizes, while maintaining predictive accuracy.