Classifier-based filters

These filters compare the observed labels against predictions obtained from one or more base classifiers.

Overview

• ClassificationFilter uses a single classifier and out-of-fold predictions.
• CVCFFilter aggregates fold-wise committee votes.
• EnsembleFiltering compares several estimators.
• INFFCFilter iteratively fuses a heterogeneous committee.
• IterativePartitioningFilter repeatedly partitions the data and checks agreement.

ClassificationFilter

Bases: BaseEstimator

Cross-validated single-classifier noise filter.

Parameters:

Name	Type	Description	Default
estimator	estimator	Base learner cloned and trained on each fold.	required
cv	int	Number of stratified folds used to generate out-of-fold predictions.	10
action	(remove, detect)	Whether noisy samples are dropped or only detected.	"remove"
random_state	int	Seed used by the stratified splitter.	33

Notes

A sample is flagged as noisy when its out-of-fold prediction differs from the observed label.

fit(X, y)

Fit the filter and cache out-of-fold predictions.

fit_resample(X, y)

Fit the filter and return the filtered or detected data.

get_filter_report()

Return a dictionary with the main fit diagnostics.

get_detection_report()

Return the stored detection report.

Summary of a single-classifier noise filtering run.

CVCFFilter

Bases: BaseEstimator

Cross-validated committees noise filter.

Parameters:

Name	Type	Description	Default
estimator	estimator	Base learner cloned for each fold of the committee.	c45_like
cv	int	Number of stratified folds used to build the committee.	10
vote_rule	(threshold, consensus)	Rule used to flag samples as noisy from the fold disagreements.	"threshold"
threshold	float	Minimum fraction of disagreeing folds required when vote_rule="threshold".	0.5
action	(remove, detect)	Whether noisy samples are dropped or only detected.	"remove"
random_state	int	Seed used by the stratified splitter.	33

Notes

Relabel is not implemented yet.

fit(X, y)

Fit the filter and cache fold-wise predictions and agreement scores.

fit_resample(X, y)

Fit the filter and return the filtered data.

get_filter_report()

Return a dictionary with the main fit diagnostics.

get_detection_report()

Return the stored detection report.

Summary of a cross-validated committees filtering run.

EnsembleFiltering

Bases: BaseEstimator

Ensemble-based noise filter using multiple classifiers.

Parameters:

Name	Type	Description	Default
estimators	sequence of estimators	Base learners combined in the ensemble committee.	required
cv	int	Number of stratified folds used to compute out-of-fold predictions.	10
mode	str	Decision rule used to flag samples as noisy. The current implementation accepts "threshold" and "consensus"; the signature default is kept for compatibility.	"S"
threshold	float	Minimum fraction of disagreeing estimators required when mode="threshold".	0.5
action	(remove, detect)	Whether noisy samples are dropped or only detected.	"remove"
random_state	int	Seed used by the stratified splitter.	33
return_noisy_samples	bool	Stored on the instance for compatibility; the current implementation does not branch on it.	False

Notes

A sample is flagged as noisy when enough estimators disagree with its observed label.

fit(X, y)

Fit the filter and cache ensemble disagreement counts.

fit_resample(X, y)

Fit the filter and return the filtered data.

get_filter_report()

Return a dictionary with the main fit diagnostics.

get_detection_report()

Return the stored detection report.

Summary of an ensemble-based noise filtering run.

INFFCFilter

Bases: BaseEstimator

Iterative fusion-of-classifiers noise filter.

Parameters:

Name	Type	Description	Default
estimators	sequence of estimators or None	Base learners used by the committee. If None, a default trio of C4.5-like tree, 1-NN and LDA is used.	None
cv	int	Number of stratified folds used inside each iteration.	10
decision_rule	(majority, consensus, threshold)	Rule used to flag a sample as noisy from the committee disagreements.	"majority"
threshold	float	Minimum disagreement fraction required when decision_rule="threshold".	0.5
action	(remove, detect)	Whether noisy samples are dropped or only detected.	"remove"
max_iter	int	Maximum number of cleaning iterations.	20
max_removed_frac	float	Stop once this fraction of the original training set has been removed.	0.5
random_state	int	Seed used by the stratified splitter in each iteration.	33

Notes

Relabel is not implemented yet.

fit(X, y)

Fit the filter and iteratively remove noisy samples.

fit_resample(X, y)

Fit the filter and return the filtered or detected data.

get_filter_report()

Return a dictionary with the main fit diagnostics.

get_detection_report()

Return the stored detection report.

Summary of an INFFC filtering run.

Per-iteration diagnostics for INFFC.

IterativePartitioningFilter

Bases: BaseEstimator

Iterative partitioning noise filter.

Parameters:

Name	Type	Description	Default
estimator	estimator	Base learner fitted on each partition.	c45_like
n_partitions	int	Number of stratified partitions built at each iteration.	10
vote_rule	(majority, consensus)	Rule used to flag a sample as noisy from the partition disagreements.	"majority"
action	(remove, detect)	Whether noisy samples are dropped or only detected.	"remove"
p_stop	float	Patience threshold expressed as a fraction of the original dataset.	0.01
k_patience	int	Number of consecutive low-yield iterations tolerated before stopping.	3
max_iter	int	Maximum number of cleaning iterations.	20
random_state	int	Seed used by the stratified splitter in each iteration.	33

Notes

Relabel is not implemented yet.

fit(X, y)

Fit the filter and iteratively partition the training data.

fit_resample(X, y)

Fit the filter and return the filtered or detected data.

get_filter_report()

Return a dictionary with the main fit diagnostics.

get_detection_report()

Return the stored detection report.

Summary of an iterative partitioning filtering run.

Per-iteration diagnostics for iterative partitioning filtering.