Supervised

information theoretical based

Algorithm	Reference
CIFE	Conditional infomax learning: An integrated framework for feature extraction and fusion
CMIM	Fast Binary Feature Selection with Conditional Mutual Information
DISR	Information-theoretic feature selection in microarray data using variable complementarity
FCBF	Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution
ICAP	A powerful feature selection approach based on mutual information
JMI	Data visualization and feature selection: New algorithms for non-gaussian data
MIFS	Using mutual information for selecting features in supervised neural net learning
MIM	Feature selection and feature extraction for text categorization
MRMR	Feature selection based on mutual information: Criteria of maxdependency, max-relevance, and min-redundancy

similarity based

Algorithm	Reference
fisher_score	R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification
reliefF	Theoretical and Empirical Analysis of ReliefF and RReliefF
trace_ratio	Trace Ratio Criterion for Feature Selection

sparse learning based

Algorithm	Reference
ll_l21	Feature Selection for Classification: A Review
ls_l21	Feature Selection for Classification: A Review
RFS	Efficient and Robust Feature Selection via Joint l2,1-Norms Minimization

statistical based

Algorithm	Reference
CFS	Feature selection for machine learning: Comparing a correlation-based filter approach to the wrapper
chi_square	Chi2: Feature selection and discretization of numeric attributes
f_score	The interpretation of population structure by f-statistics with special regard to systems of mating
gini_index	Variabilita e Mutabilita
t_score	Statistics and data analysis in geology

streaming

Algorithm	Reference
alpha_investing	Streaming Feature Selection using Alpha-investing

structure

Algorithm	Reference
graph_fs	Feature Selection for Classification: A Review
group_fs	Feature Selection for Classification: A Review
tree_fs	Feature Selection for Classification: A Review

wrapper

Algorithm	Reference
decision_tree_backward	An Introduction to Variable and Feature Selection
decision_tree_forward	An Introduction to Variable and Feature Selection
svm_backward	An Introduction to Variable and Feature Selection
svm_forward	An Introduction to Variable and Feature Selection

Unsupervised

similarity based

Algorithm	Reference
lap_score	Laplacian score for feature selection
SPEC	Spectral Feature Selection for Supervised and Unsupervised Learning

sparse learning based

Algorithm	Reference
MCFS	Unsupervised feature selection for multi-cluster data
NDFS	Unsupervised Feature Selection Using Nonnegative Spectral Analysis
UDFS	l2,1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning

statistical based

Algorithm	Reference
low_variance	F. Pedregosa el al. Scikit-learn: Machine learning in Python