Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning
| Autor: | Wouter van Loon, Mark de Rooij, Botond Szabo, Marjolein Fokkema |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science media_common.quotation_subject FEATURE SELECTION GROUP LASSO MULTI-VIEW LEARNING STACKED GENERALIZATION Machine Learning (stat.ML) 02 engineering and technology Machine learning computer.software_genre Logistic regression Machine Learning (cs.LG) Methodology (stat.ME) Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Medical imaging Multi-view learning Stacked generalization Special case Function (engineering) Statistics - Methodology Selection (genetic algorithm) media_common Modalities Data collection business.industry 020206 networking & telecommunications Group lasso Hardware and Architecture Signal Processing Feature selection 020201 artificial intelligence & image processing False positive rate Artificial intelligence business computer Software Information Systems |
| Popis: | In biomedical research, many different types of patient data can be collected, such as various types of omics data and medical imaging modalities. Applying multi-view learning to these different sources of information can increase the accuracy of medical classification models compared with single-view procedures. However, collecting biomedical data can be expensive and/or burdening for patients, so that it is important to reduce the amount of required data collection. It is therefore necessary to develop multi-view learning methods which can accurately identify those views that are most important for prediction. In recent years, several biomedical studies have used an approach known as multi-view stacking (MVS), where a model is trained on each view separately and the resulting predictions are combined through stacking. In these studies, MVS has been shown to increase classification accuracy. However, the MVS framework can also be used for selecting a subset of important views. To study the view selection potential of MVS, we develop a special case called stacked penalized logistic regression (StaPLR). Compared with existing view-selection methods, StaPLR can make use of faster optimization algorithms and is easily parallelized. We show that nonnegativity constraints on the parameters of the function which combines the views play an important role in preventing unimportant views from entering the model. We investigate the performance of StaPLR through simulations, and consider two real data examples. We compare the performance of StaPLR with an existing view selection method called the group lasso and observe that, in terms of view selection, StaPLR is often more conservative and has a consistently lower false positive rate. Comment: 26 pages, 9 figures. Accepted manuscript |
| Databáze: | OpenAIRE |
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