Cross validation folds used to evaluate algorithm performance.
Iterations
Number of times each instance participates in the test fold. Each iteration demands
the repetition of the whole process, including hyper-parameter optimization, if enabled.
Metric
Metric used to evaluate algorithm performance.
Help Algorithms
Select which classification algorithms will be evaluated.
Help Measures
Select which hardness measures will serve as meta-features for instance space analysis input.
Help Hyper-parameter optimization
Evaluations
Number of iterations in Bayesian optimization process.
Help Feature Selection
CIFE
Conditional Infomax Feature Extraction.
CMIM
Conditional Mutual Information Maximization.
DISR
Double Input Symmetrical Relevance.
ICAP
Interaction Capping.
JMI
Joint Mutual Information.
MIFS
Mutual Information Feature Selection.
MIM
Mutual Information Maximization.
MRMR
Minimum Redundancy Maximum Relevance.
Reference: Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang,
and Huan Liu. 2017. Feature Selection: A Data Perspective. ACM Comput. Surv. 50, 6,
Article 94 (January 2018), 45 pages.