86,866 research outputs found
High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression
Motivation: The high dimensionality of genomic data calls for the development
of specific classification methodologies, especially to prevent over-optimistic
predictions. This challenge can be tackled by compression and variable
selection, which combined constitute a powerful framework for classification,
as well as data visualization and interpretation. However, current proposed
combinations lead to instable and non convergent methods due to inappropriate
computational frameworks. We hereby propose a stable and convergent approach
for classification in high dimensional based on sparse Partial Least Squares
(sparse PLS). Results: We start by proposing a new solution for the sparse PLS
problem that is based on proximal operators for the case of univariate
responses. Then we develop an adaptive version of the sparse PLS for
classification, which combines iterative optimization of logistic regression
and sparse PLS to ensure convergence and stability. Our results are confirmed
on synthetic and experimental data. In particular we show how crucial
convergence and stability can be when cross-validation is involved for
calibration purposes. Using gene expression data we explore the prediction of
breast cancer relapse. We also propose a multicategorial version of our method
on the prediction of cell-types based on single-cell expression data.
Availability: Our approach is implemented in the plsgenomics R-package.Comment: 9 pages, 3 figures, 4 tables + Supplementary Materials 8 pages, 3
figures, 10 table
Learning Credible Models
In many settings, it is important that a model be capable of providing
reasons for its predictions (i.e., the model must be interpretable). However,
the model's reasoning may not conform with well-established knowledge. In such
cases, while interpretable, the model lacks \textit{credibility}. In this work,
we formally define credibility in the linear setting and focus on techniques
for learning models that are both accurate and credible. In particular, we
propose a regularization penalty, expert yielded estimates (EYE), that
incorporates expert knowledge about well-known relationships among covariates
and the outcome of interest. We give both theoretical and empirical results
comparing our proposed method to several other regularization techniques.
Across a range of settings, experiments on both synthetic and real data show
that models learned using the EYE penalty are significantly more credible than
those learned using other penalties. Applied to a large-scale patient risk
stratification task, our proposed technique results in a model whose top
features overlap significantly with known clinical risk factors, while still
achieving good predictive performance
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