Predictive learning with structured (grouped) data.
Neural Netw
; 22(5-6): 766-73, 2009.
Article
in En
| MEDLINE
| ID: mdl-19596546
ABSTRACT
Many applications of machine learning involve sparse and heterogeneous data. For example, estimation of diagnostic models using patients' data from clinical studies requires effective integration of genetic, clinical and demographic data. Typically all heterogeneous inputs are properly encoded and mapped onto a single feature vector, used for estimating a classifier. This approach, known as standard inductive learning, is used in most application studies. Recently, several new learning methodologies have emerged. For instance, when training data can be naturally separated into several groups (or structured), we can view model estimation for each group as a separate task, leading to a Multi-Task Learning framework. Similarly, a setting where the training data are structured, but the objective is to estimate a single predictive model (for all groups), leads to the Learning with Structured Data and SVM+ methodology recently proposed by Vapnik [(2006). Empirical inference science afterword of 2006. Springer]. This paper describes a biomedical application of these new data modeling approaches for modeling heterogeneous data using several medical data sets. The characteristics of group variables are analyzed. Our comparisons demonstrate the advantages and limitations of these new approaches, relative to standard inductive SVM classifiers.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Artificial Intelligence
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Female
/
Humans
/
Male
Language:
En
Journal:
Neural Netw
Journal subject:
NEUROLOGIA
Year:
2009
Document type:
Article
Affiliation country: