Feature Selection ToolboxFST3 Library / DocumentationShare

Class List

Here are the classes, structs, unions and interfaces with brief descriptions:
FST::Branch_And_Bound_Predictor< RETURNTYPE, DIMTYPE >Defines Interface for Implementations of Prediction Mechanism in Fast Branch and Bound & Branch and Bound with Partial Prediction
FST::Branch_And_Bound_Predictor_Averaging< RETURNTYPE, DIMTYPE >Averaging Prediction Mechanism in Fast Branch and Bound & Branch and Bound with Partial Prediction
FST::Candidate_Evaluator_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >Implements concurrent evaluation of a criterion on a set of subset candidates
FST::Candidate_Evaluator_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::CandidateResultTemporary storage of subset candidate evaluation results
FST::Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Abstract class, defines interface for classifier implementations (mainly to be used in wrappers)
FST::Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >Implements k-Nearest Neighbor classifier
FST::Classifier_LIBSVM< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Wraps external Support Vector Machine implementation (in LibSVM) to serve as FST3 classifier
FST::Classifier_Multinomial_NaiveBayes< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Implements Naive-like Bayes classifier based on multinomial model
FST::Classifier_Normal_Bayes< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Implements Bayes classifier based on normal (gaussian) model
FST::ClonableAbstract class, defines interface for classes that may need to get cloned
FST::Criterion< RETURNTYPE, SUBSET >Abstract class, defines interface for feature selection criterion classes
FST::Criterion_Filter< RETURNTYPE, SUBSET >Abstract class, defines interface for classes implementing "independent" feature selection criteria
FST::Criterion_Multinomial< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Abstract class, defines interface for feature selection criterion implementations based on multinomial model
FST::Criterion_Multinomial_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Implements Bhattacharyya distance based on multinomial model to serve as feature selection criterion
FST::Criterion_Multinomial_MI< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Implements individual Mutual Information based on multinomial model to serve as feature selection criterion in Best Individual Feature setting (feature ranking) only
FST::Criterion_Negative< CRITERION, RETURNTYPE, SUBSET >Returns the negative of another Criterion's result
FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Abstract class, defines interface for feature selection criterion implementations based on normal model
FST::Criterion_Normal_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Implements Bhattacharyya distance based on normal (gaussian) model to serve as feature selection criterion
FST::Criterion_Normal_Divergence< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Implements Divergence (distance) based on normal (gaussian) model to serve as feature selection criterion
FST::Criterion_Normal_GMahalanobis< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Implements Generalized Mahalanobis distance based on normal (gaussian) model to serve as feature selection criterion
FST::Criterion_Subset_Size< RETURNTYPE, SUBSET >Returns negative subset size - rates smaller subsets higher
FST::Criterion_Sum_Of_Weights< RETURNTYPE, DIMTYPE, SUBSET >Returns sum of pre-specified feature weights for features in the evaluated subset
FST::Criterion_Wrapper< RETURNTYPE, SUBSET, CLASSIFIER, DATAACCESSOR >Wraps Classifier objects to serve as feature selection criterion
FST::Criterion_Wrapper_Bias_Estimate< RETURNTYPE, SUBSET, CLASSIFIER, DATAACCESSOR >Wrapper Bias Evaluator - returns bias estimate (difference of classifier accuracy on training and validation data parts)
FST::Data_Accessor< DATATYPE, IDXTYPE >Abstract class, defines interface for data access implementations
FST::Data_Accessor_Splitting< DATATYPE, IDXTYPE, INTERVALCONTAINER >Partly-abstract class, defines support for data splitting
FST::Data_Accessor_Splitting_Mem< DATATYPE, IDXTYPE, INTERVALCONTAINER >Implements data access to data cached entirely in memory, concrete file type support is delegated to derived classes
FST::Data_Accessor_Splitting_MemARFF< DATATYPE, IDXTYPE, INTERVALCONTAINER >Implements data access to data cached entirely in memory, read once from a ARFF file
FST::Data_Accessor_Splitting_MemTRN< DATATYPE, IDXTYPE, INTERVALCONTAINER >Implements data access to data cached entirely in memory, read once from a TRN file
FST::Data_File_ARFFARFF data format filter
FST::Data_File_ARFF_FeatureARFF data format filter - representation of an attribute
FST::Data_File_ARFF_RecordARFF data format filter - representation of a record
FST::Data_File_TRN< DATATYPE, IDXTYPE >TRN data format filter
FST::Data_Interval< IDXTYPE >Support structure to hold data interval (referring into data held by data accessor)
FST::Data_Intervaller< CONTAINER, IDXTYPE >Container to hold list of Data_Interval; implements nested interval reduction
FST::Data_Scaler< DATATYPE >Abstract class, defines interface for data scaling implementations (for use in data accessors)
FST::Data_Scaler_to01< DATATYPE >Implements data normalization (of all feature values) to interval [0,1]
FST::Data_Scaler_void< DATATYPE >Void data scaler, to bypass data normalization
FST::Data_Scaler_white< DATATYPE, IDXTYPE >Implements data whitening (as result, each feature mean value is 0 with normalized stddev)
FST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >Abstract class, defines interface for data splitting implementations (for use in data accessors)
FST::Data_Splitter_5050< INTERVALCONTAINER, IDXTYPE >Implements train/test data splitting: first 50% of original data for training, second 50% for testing, each (data)class split separately
FST::Data_Splitter_CV< INTERVALCONTAINER, IDXTYPE >Implements train/test data splitting: by means of k-fold cross-validation
FST::Data_Splitter_Holdout< INTERVALCONTAINER, IDXTYPE >Implements train/test data splitting: use initial x% of original data for training, the rest for testing, each (data)class split separately
FST::Data_Splitter_Leave1Out< INTERVALCONTAINER, IDXTYPE >Implements train/test data splitting: by means of leave-one-out
FST::Data_Splitter_RandomRandom< INTERVALCONTAINER, IDXTYPE, BINTYPE >Implements train/test data splitting: use randomly chosen x% of data samples for training and another y% of data for testing, without overlaps, separately in each class
FST::Data_Splitter_Resub< INTERVALCONTAINER, IDXTYPE >Implements train/test data splitting: use all data both for training and then once more for testing
FST::Data_Splitter_TrainRandom_TestFixed< INTERVALCONTAINER, IDXTYPE, BINTYPE >Implements train/test data splitting: equal to Data_Splitter_RandomRandom except that the same test data is returned in each loop
FST::Data_Accessor_Splitting< DATATYPE, IDXTYPE, INTERVALCONTAINER >::DataSplitData splitting support structure; holds one set of intervals (train, test) per each splitting depth and (data)class
FST::Distance< DATATYPE, DIMTYPE, SUBSET >Abstract class, defines interface for distance evaluators to be used in Classifier_kNN
FST::Distance_Euclid< DATATYPE, DIMTYPE, SUBSET >Implements Euclidean (L_2) distance
FST::Distance_L1< DATATYPE, DIMTYPE, SUBSET >Implements L_1 distance
FST::Distance_Lp< DATATYPE, REALTYPE, DIMTYPE, SUBSET, numerator, denominator >Implements L_{p} distance, p equals numerator/denominator (template parameters)
FST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >::FeatureStatStructure to gather feature occurence statistics over probe subset evaluations
FST::fst_errorFST3 specific error passing class
FST::Indexed_Matrix< DATATYPE, DIMTYPE, SUBSET >Matrix representation and operations, allows operation in selected subspace
FST::Indexed_Vector< DATATYPE, DIMTYPE, SUBSET >Vector representation and operations, allows operation in selected subspace
FST::Model< SUBSET, DATAACCESSOR >Abstract class, defines interface for data model implementations
FST::Model_Multinomial< DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Implements multinomial model
FST::Model_Normal< DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >Implements normal (gaussian) model
FST::Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >::NeighbourHolds information on distance and data-class membership of neighbors processed in Classifier_kNN
FST::Search_Branch_And_Bound< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::NodeStructure representing search tree node
FST::Search_BIF< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::OneFeatureStructure to hold [feature,criterion value] pair while ranking features in Search_BIF
FST::Search_BIF_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::OneFeatureStructure to hold [feature,criterion value] pair while ranking features in Search_BIF_Threaded
FST::Search_SFFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >::OneSubsetStructure to hold [subset,criterion value] temporary solutions in the course of search
FST::Search_SFRS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >::OneSubsetStructure to hold [subset,criterion value] temporary solutions in the course of search
FST::Search_SFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >::OneSubsetStructure to hold [subset,criterion value] temporary solutions in the course of search
FST::Classifier_LIBSVM< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::ParameterSetNested class to hold parameter candidates in the course of optimize_parameters() run
FST::Result_Tracker< RETURNTYPE, SUBSET >Abstract class, defines interface for classes that enable collecting multiple results
FST::Result_Tracker_Dupless< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >Collects multiple results, avoiding duplicates
FST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >Collects evaluated subsets to eventually provide Dependency-Aware Feature Ranking coefficients
FST::Result_Tracker_Regularizer< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, CRITERION >Collects multiple results. Enables eventual selection of alternative solution based on secondary criterion
FST::Result_Tracker_Stability_Evaluator< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >Collects multiple results to evaluate various stability measures on the colleciton
FST::Result_Tracker< RETURNTYPE, SUBSET >::ResultRecStructure to hold [subset,criterion value] temporary solutions in the course of search
FST::Search< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Abstract class, defines interface for search method implementations
FST::Search_BIF< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Implements Best Individual Features, i.e., individual feature ranking
FST::Search_BIF_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >Implements Best Individual Features, i.e., individual feature ranking
FST::Search_Branch_And_Bound< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Implements Branch and Bound template method as basis for more advanced B&B implementations
FST::Search_Branch_And_Bound_Basic< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Implements Branch and Bound Basic method, i.e., with randomized node ordering
FST::Search_Branch_And_Bound_Fast< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, PREDICTOR >Implements Fast Branch and Bound, i.e., B&B with full utilization of prediction mechanism
FST::Search_Branch_And_Bound_Improved< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Implements Improved Branch and Bound (IBB) method, i.e., with fully computed node ordering
FST::Search_Branch_And_Bound_Improved_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >Implements threaded Improved Branch and Bound (IBB) method, i.e., with fully computed node ordering
FST::Search_Branch_And_Bound_Partial_Prediction< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, PREDICTOR >Implements Branch and Bound with Partial Prediction (BBPP) method, i.e., with predicted node ordering
FST::Search_DOS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >Implements Dynamic_Oscillating_Search
FST::Search_Exhaustive< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Implements exhaustive (optimal) search yielding optimal feature subset with respect to chosen criterion
FST::Search_Exhaustive_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >Implements threaded version of exhaustive (optimal) search yielding optimal feature subset with respect to chosen criterion
FST::Search_Monte_Carlo< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Repeatedly samples random subsets to eventually yield the one with highest criterion value
FST::Search_Monte_Carlo_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >Implements threaded version of randomized search that repeatedly samples random subsets to eventually yield the one with highest criterion value
FST::Search_OS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >Implements Oscillating_Search
FST::Search_Sequential< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >Abstract class, defines interface for sequential search method implementations, provides common implementation of the "sequential step" operation
FST::Search_SFFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >Implements Sequential_Forward_Floating_Search and Sequential_Backward_Floating_Search
FST::Search_SFRS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >Implements Sequential_Forward_Retreating_Search and Sequential_Backward_Retreating_Search
FST::Search_SFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >Implements Sequential_Forward_Selection and Sequential_Backward_Selection
FST::Sequential_Step< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Abstract class, defines interface for implementations of one selection step in sequential search type of methods
FST::Sequential_Step_Ensemble< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Implements voting ensemble selection step in sequential search type of methods to possibly improve robustness and stability of feature selection result
FST::Sequential_Step_Hybrid< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, FILTERCRITERION >Implements hybrid selection step in sequential search type of methods
FST::Sequential_Step_Straight< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >Implements standard sequential selection step in sequential search type of methods
FST::Sequential_Step_Straight_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >Implements threaded version of sequential selection step in sequential search type of methods
FST::StopWatchSimple stopwatch to measure run time of search algorithms
FST::Subset< BINTYPE, DIMTYPE >Stores the info on currently selected features, enables generating permutations, etc
FST::Sequential_Step_Ensemble< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::SubsetCandidateNested class to hold feature/subset candidate info in the course of the ensemble voting process
FST::Sequential_Step_Hybrid< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, FILTERCRITERION >::SubsetCandidateNested class to hold [Subset,criterion value] pair in the course of hybrid feature candidate evaluation in Sequential_Step_Hybrid
FST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >::SubSizeStatStructure to gather probe subset cardinality statistics
FST::syncoutSynchronized output singleton class to enable concurrent textual reporting
FST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >::TClassSplitterData splitting support structure; holds one set of intervals (train, test) per each (data)class
FST::Search_Exhaustive_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::ThreadLocalThread-local storage of current subset candidate, criterion clone, and tracker clone
FST::Candidate_Evaluator_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::ThreadLocalThread-local storage of current subset candidate, criterion clone, and tracker clone
FST::Search_Monte_Carlo_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::ThreadLocalThread-local storage of current subset candidate, criterion clone, and tracker clone
FST::ThreadPool< max_threads >Implements thread scheduler that assigns jobs up to maximum number of threads allowed
FST::ThreadPool< max_threads >::ThreadsArrayStructure to keep status of threads in the pool

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