branch_and_bound_predictor.hpp | Interface for Implementations of Prediction Mechanism in Fast Branch & Bound and Branch & Bound with Partial Prediction |

branch_and_bound_predictor_averaging.hpp | Averaging Prediction Mechanism for use in Fast Branch & Bound and Branch & Bound with Partial Prediction |

candidate_evaluator_threaded.hpp | Implements concurrent evaluation of a criterion on a set of subset candidates |

classifier.hpp | Defines interface for classifier implementations |

classifier_knn.hpp | Implements k-Nearest Neighbor classifier |

classifier_multinom_naivebayes.hpp | Implements Naive-like Bayes classifier based on multinomial model |

classifier_normal_bayes.hpp | Implements Bayes classifier based on normal (gaussian) model |

classifier_svm.hpp | Wraps external Support Vector Machine implementation (in LibSVM) to serve as FST3 classifier |

clonable.hpp | Defines interface for classes that may need to get cloned |

criterion.hpp | Defines interface for feature selection criterion classes |

criterion_multinom.hpp | Defines interface for feature selection criterion implementations based on multinomial model |

criterion_multinom_bhattacharyya.hpp | Implements Bhattacharyya distance based on multinomial model to serve as feature selection criterion |

criterion_negative.hpp | Criterion_Negative returns the negative of another Criterion's result |

criterion_normal.hpp | Defines interface for feature selection criterion implementations based on normal model |

criterion_normal_bhattacharyya.hpp | Implements Bhattacharyya distance based on normal (gaussian) model to serve as feature selection criterion |

criterion_normal_divergence.hpp | Implements Divergence (distance) based on normal (gaussian) model to serve as feature selection criterion |

criterion_normal_gmahalanobis.hpp | Implements Generalized Mahalanobis distance based on normal (gaussian) model to serve as feature selection criterion |

criterion_subsetsize.hpp | Criterion_Subset_Size returns negative subset size - rates smaller subsets higher |

criterion_sumofweights.hpp | Criterion_Sum_Of_Weights returns sum of pre-specified feature weights for features in the evaluated subset |

criterion_wrapper.hpp | Classifier_Wrapper adapts Classifier objects to serve as feature selection criterion |

criterion_wrapper_bias_estimate.hpp | Wrapper Bias Evaluator - returns classifier bias estimate (difference between accuracy on training and validation data parts) |

data_accessor.hpp | Defines interface for data access implementations |

data_accessor_splitting.hpp | Defines support for data splitting |

data_accessor_splitting_mem.hpp | Implements data access to data cached entirely in memory, concrete file type support is delegated to derived classes |

data_accessor_splitting_memARFF.hpp | Implements data access to data cached entirely in memory, read once from ARFF files |

data_accessor_splitting_memTRN.hpp | Implements data access to data cached entirely in memory, read once from TRN files |

data_file_ARFF.hpp | Enables data accessor objects to read ARFF (Weka) type of data files |

data_file_TRN.hpp | Enables data accessor objects to read TRN type of data files |

data_intervaller.hpp | Container to hold a list of Data_Interval; implements nested interval reduction |

data_scaler.hpp | Defines interface for data scaling implementations (for use in data accessors) |

data_scaler_to01.hpp | Implements data normalization (of all feature values) to interval [0,1] |

data_scaler_void.hpp | Void data scaler, to bypass data normalization |

data_scaler_white.hpp | Implements data whitening (as result, each feature mean value is 0 with normalized stddev) |

data_splitter.hpp | Defines interface for data splitting implementations (for use in data accessors) |

data_splitter_5050.hpp | Implements train/test data splitting: first 50% of original data for training, second 50% for testing |

data_splitter_cv.hpp | Implements train/test data splitting: by means of k-fold cross-validation |

data_splitter_holdout.hpp | Implements train/test data splitting: use initial x% of original data for training, the rest for testing |

data_splitter_leave1out.hpp | Implements train/test data splitting: by means of leave-one-out |

data_splitter_randfix.hpp | Implements train/test data splitting: equal to Data_Splitter_RandomRandom except that the same test data is returned in each loop |

data_splitter_randrand.hpp | Implements train/test data splitting: use randomly chosen x% of data samples for training and another y% of data for testing, without overlaps |

data_splitter_resub.hpp | Implements train/test data splitting: use all data both for training and then once more for testing |

demo10.cpp | Example 10: Basic Filter-based feature selection |

demo11.cpp | Example 11: Wrapper-based feature selection with Floating Search |

demo11t.cpp | Example 11t: Threaded wrapper-based feature selection with Floating Search |

demo12t.cpp | Example 12t: Threaded SVM-wrapper-based feature selection with Dynamic Oscillating Search |

demo20.cpp | Example 20: Retreating Sequential Search |

demo21.cpp | Example 21: Generalized sequential feature subset search |

demo22.cpp | Example 22: Randomized feature selection with Oscillating Search |

demo23.cpp | Example 23: Combined feature subset contents, size and SVM parameters optimization |

demo24.cpp | Example 24: Monte Carlo - random feature subset search |

demo24t.cpp | Example 24t: Threaded Monte Carlo - random feature subset search |

demo25t.cpp | Example 25t: Fast pre-selection followed by Dynamic Oscillating Search |

demo26.cpp | Example 26: Wrapper-based (normal Bayes) feature selection with Floating Search, verified by 2 classifiers |

demo30.cpp | Example 30: Feature selection on binary and/or natural-valued data |

demo31.cpp | Example 31: Individual ranking (BIF) in very high-dimensional feature selection |

demo32t.cpp | Example 32t: Threaded individual ranking (BIF) with SVM wrapper in very high-dimensional feature selection |

demo33.cpp | Example 33: Oscillating Search in very high-dimensional feature selection |

demo33t.cpp | Example 33t: Threaded Oscillating Search in very high-dimensional feature selection |

demo34.cpp | Example 34: Dependency-Aware Feature Ranking (DAF0) |

demo35t.cpp | Example 35t: Dependency-Aware Feature Ranking (DAF1) to enable Wrapper based FS on very-high-dimensional data |

demo40.cpp | Example 40: Exhaustive (optimal) feature selection |

demo40t.cpp | Example 40t: Threaded Exhaustive (optimal) feature selection |

demo41.cpp | Example 41: Improved Branch and Bound (IBB) optimal feature selection |

demo42.cpp | Example 42: Branch and Bound with Partial Prediction (BBPP) optimal feature selection |

demo43.cpp | Example 43: Fast Branch and Bound (FBB) optimal feature selection |

demo50.cpp | Example 50: Voting ensemble of criteria |

demo51.cpp | Example 51: (DOS) Result regularization using secondary criterion |

demo52t.cpp | Example 52t: (Threaded SFRS) Result regularization using secondary criterion |

demo53.cpp | Example 53: Hybrid feature selection |

demo54.cpp | Example 54: Feature Selection Stability Evaluation |

demo55.cpp | Example 55: Evaluating Similarity of Two Feature Selection Processes |

demo56.cpp | Example 56: Revealing feature selection results' bias |

demo60.cpp | Example 60: Detecting alternative feature selection results with equal criterion value |

demo61.cpp | Example 61: Feature selection that respects pre-specified feature weights |

demo62.cpp | Example 62: (Missing data substitution) Combined feature subset contents, size and SVM parameters optimization |

demo63.cpp | Example 63: Classification of new data samples on the selected subspace |

distance.hpp | Defines interface for distance evaluators (to be used primarily in Classifier_kNN) |

distance_euclid.hpp | Implements Euclidean (L_2) distance |

distance_L1.hpp | Implements L_1 distance |

distance_Lp.hpp | Implements L_{p} distance, p equals numerator/denominator (template parameters) |

error.hpp | FST3 specific error passing class |

global.cpp | Global definitions |

global.hpp | Global definitions |

indexed_matrix.hpp | Matrix representation and operations, allows operation in selected subspace |

indexed_vector.hpp | Vector representation and operations, allows operation in selected subspace |

model.hpp | Defines interface for data model implementations |

model_multinom.hpp | Implements multinomial model |

model_normal.hpp | Implements normal (gaussian) model |

result_tracker.hpp | Defines interface for classes that enable collecting multiple results |

result_tracker_dupless.hpp | Enables collecting multiple results |

result_tracker_feature_stats.hpp | Computes feature occurence statistics in a series of evaluated subsets |

result_tracker_regularizer.hpp | Enables eventual selection of a different subset |

result_tracker_stabileval.hpp | Enables collecting multiple results, then evaluating various stability measures |

search.hpp | Defines interface for search method implementations |

search_bif.hpp | Implements Best Individual Features, i.e., individual feature ranking |

search_bif_threaded.hpp | Threaded implementation of Best Individual Features, i.e., individual feature ranking |

search_branch_and_bound.hpp | Implements Branch and Bound template method as basis for more advanced B&B implementations |

search_branch_and_bound_basic.hpp | Implements Branch and Bound Basic method, i.e., with randomized node ordering |

search_branch_and_bound_fast.hpp | Implements Fast Branch and Bound, i.e., B&B with full utilization of prediction mechanism |

search_branch_and_bound_improved.hpp | Implements Improved Branch and Bound (IBB) method, i.e., with fully computed node ordering |

search_branch_and_bound_improved_threaded.hpp | Implements Threaded version of Improved Branch and Bound (IBB) method, i.e., with fully computed node ordering |

search_branch_and_bound_partial_prediction.hpp | Implements Branch and Bound with Partial Prediction (BBPP) method, i.e., with predicted node ordering |

search_exhaustive.hpp | Defines interface for search method implementations |

search_exhaustive_threaded.hpp | Implements threaded version of exhaustive (optimal) search yielding optimal feature subset with respect to chosen criterion |

search_monte_carlo.hpp | Repeatedly samples random subsets to eventually yield the one with highest criterion value |

search_monte_carlo_threaded.hpp | Implements threaded version of randomized search that repeatedly samples random subsets to eventually yield the one with highest criterion value |

search_seq.hpp | Defines interface for sequential search method implementations |

search_seq_dos.hpp | Implements Dynamic_Oscillating_Search |

search_seq_os.hpp | Implements Oscillating_Search |

search_seq_sffs.hpp | Implements Sequential_Forward_Floating_Search and Sequential_Backward_Floating_Search |

search_seq_sfrs.hpp | Implements Sequential_Forward_Retreating_Search and Sequential_Backward_Retreating_Search |

search_seq_sfs.hpp | Implements Sequential_Forward_Selection and Sequential_Backward_Selection |

seq_step.hpp | Defines interface for implementations of one selection step in sequential search type of methods |

seq_step_ensemble.hpp | Implements voting ensemble selection step in sequential search type of methods |

seq_step_hybrid.hpp | Implements hybrid selection step in sequential search type of methods |

seq_step_straight.hpp | Implements standard sequential selection step in sequential search type of methods |

seq_step_straight_threaded.hpp | Implements threaded version of sequential selection step in sequential search type of methods |

stopwatch.hpp | Simple tool to measure run time |

subset.hpp | Stores the info on currently selected features, enables generating permutations, etc |

thread_pool.hpp | Implements thread scheduler that assigns jobs up to maximum number of threads allowed |

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