Supervised Model-based Learning Task
(Redirected from Model-based Supervised Learning Task)
- It can range from being a Supervised Model-based Classification Task to being a Supervised Model-based Ranking Task to being a Supervised Model-based Estimation Task.
- It can range from being a Fully-Supervised Model-based Learning Task to being a Semi-Supervised Model-based Learning Task.
- It can range from being a Supervised Single-Model Learning Task to being a Supervised Ensemble-Model Learning Task.
- It can be solved by a Model-based Supervised Learning System (that implements a model-based supervised learning algorithm).
- See: Model-based Learning Task, Predictive Function, Classification Function Learning, Estimation Function Learning Task.
- (Hoshida et al., 2007) ⇒ Yujin Hoshida, Jean-Philippe Brunet, Pablo Tamayo, Todd R. Golub, and Jill P. Mesirov. (2007). “Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets.” In: PLoS ONE 2(11). doi:10.1371/journal.pone.0001195
- ABSTRACT: Whole genome expression profiles are widely used to discover molecular subtypes of diseases. A remaining challenge is to identify the correspondence or commonality of subtypes found in multiple, independent data sets generated on various platforms. While model-based supervised learning is often used to make these connections, the models can be biased to the training data set and thus miss inherent, relevant substructure in the test data. Here we describe an unsupervised subclass mapping method (SubMap), which reveals common subtypes between independent data sets. The subtypes within a data set can be determined by unsupervised clustering or given by predetermined phenotypes before applying SubMap. We define a measure of correspondence for subtypes and evaluate its significance building on our previous work on gene set enrichment analysis. The strength of the SubMap method is that it does not impose the structure of one data set upon another, but rather uses a bi-directional approach to highlight the common substructures in both. We show how this method can reveal the correspondence between several cancer-related data sets. Notably, it identifies common subtypes of breast cancer associated with estrogen receptor status, and a subgroup of lymphoma patients who share similar survival patterns, thus improving the accuracy of a clinical outcome predictor.