TY  - BOOK
AB  - Most existing feature selection methods are insufficient for analytic
purposes as soon as high dimensional data or redundant sensor signals are dealt
with since features can be selected due to spurious effects or correlations
rather than causal effects. To support the finding of causal features in
biomedical experiments, we hereby present FRI, an open source Python library
that can be used to identify all-relevant variables in linear classification
and (ordinal) regression problems. Using the recently proposed feature
relevance method, FRI is able to provide the base for further general
experimentation or in specific can facilitate the search for alternative
biomarkers. It can be used in an interactive context, by providing model
manipulation and visualization methods, or in a batch process as a filter
method.
DA  - 2019
DO  - 10.1109/CIBCB.2019.8791489
KW  - global feature relevance
KW  - feature selection
KW  - interpretability
KW  - interactive biomarker discovery
LA  - eng
PY  - 2019
TI  - FRI - Feature Relevance Intervals for Interpretable and Interactive Data  Exploration
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29354564
Y2  - 2024-11-22T01:21:53
ER  -