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CAFE-Map: Context Aware Feature Mapping for mining high dimensional biomedical data
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DOI：10.1016/j.compbiomed.2016.10.006.png)
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摘要
En 中文
Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducin, the number of features used in classification or other machine learning tasks, it allows us to extract meaningfu biological and medical information from a machine learning model. Most existing approaches in this domain d( not directly model the fact that the relative importance of features can be different in different regions of tin. feature space. In this work, we present a context aware feature ranking algorithm called CAFE-Map. CAFE-Map is a locally linear feature ranking framework that allows recognition of important features in any given region o the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFE-Map on a number of toy and real world biomedica data sets. Our comparative study with a number of published methods shows that CAFE-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFE-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFE-Map provides a more in-depth analysis of feature ranking at the level of individual examples.
Keyword:
FEATURE-SELECTION
GENE-EXPRESSION
PROSTATE-CANCER
MASS-SPECTROMETRY
BIOMARKER DISCOVERY
DIAGNOSIS
CLASSIFICATION
PREDICTION
ASSOCIATION
METHYLATION
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