Ensemble framework for Supervised Learning classification problems

Supervised learning classification algorithms are performed on bootstrap replicates and an ensemble classifier is built and evaluated across these variants.

Usage,

splendid(
  data,
  class,
  algorithms = NULL,
  n = 1,
  seed_boot = NULL,
  seed_samp = NULL,
  seed_alg = NULL,
  convert = FALSE,
  rfe = FALSE,
  ova = FALSE,
  standardize = FALSE,
  sampling = c("none", "up", "down", "smote"),
  stratify = FALSE,
  plus = TRUE,
  threshold = 0,
  trees = 100,
  tune = FALSE,
  top = 3,
  seed_rank = 1,
  sequential = FALSE
)

Arguments

data: data frame with rows as samples, columns as features
class: true/reference class vector used for supervised learning
algorithms: character vector of algorithms to use for supervised learning. See Algorithms section for possible options. By default, this argument is NULL, in which case all algorithms are used.
n: number of bootstrap replicates to generate
seed_boot: random seed used for reproducibility in bootstrapping training sets for model generation
seed_samp: random seed used for reproducibility in subsampling training sets for model generation
seed_alg: random seed used for reproducibility when running algorithms with an intrinsic random element (random forests)
convert: logical; if TRUE, converts all categorical variables in data to dummy variables. Certain algorithms only work with such limitations (e.g. LDA).
rfe: logical; if TRUE, run Recursive Feature Elimination as a feature selection method for "lda", "rf", and "svm" algorithms.
ova: logical; if TRUE, a One-Vs-All classification approach is performed for every algorithm in algorithms. The relevant results are prefixed with the string ova_.
standardize: logical; if TRUE, the training sets are standardized on features to have mean zero and unit variance. The test sets are standardized using the vectors of centers and standard deviations used in corresponding training sets.
sampling: the default is "none", in which no subsampling is performed. Other options include "up" (Up-sampling the minority class), "down" (Down-sampling the majority class), and "smote" (synthetic points for the minority class and down-sampling the majority class). Subsampling is only applicable to the training set.
stratify: logical; if TRUE, the bootstrap resampling is performed within each strata of class to ensure the bootstrap sample contains the same proportions of each strata as the original data.
plus: logical; if TRUE (default), the .632+ estimator is calculated. Otherwise, the .632 estimator is calculated.
threshold: a number between 0 and 1 indicating the lowest maximum class probability below which a sample will be unclassified.
trees: number of trees to use in "rf" or boosting iterations (trees) in "adaboost"
tune: logical; if TRUE, algorithms with hyperparameters are tuned
top: the number of highest-performing algorithms to retain for ensemble
seed_rank: random seed used for reproducibility in rank aggregation of ensemble algorithms
sequential: logical; if TRUE, a sequential model is fit on the algorithms that had the best performance with one-vs-all classification.

Value

A nested list with five elements

models: A list with an element for each algorithm, each of which is a list with length n. Shows the model object for each algorithm and bootstrap replicate on the training set.
preds: A list with an element for each algorithm, each of which is a list with length n. Shows the predicted classes for each algorithm and bootstrap replicate on the test set.
evals: For each bootstrap sample, we can calculate various evaluation measures for the predicted classes from each algorithm. Evaluation measures include macro-averaged precision/recall/F1-score, micro-averaged precision, and (micro-averaged MCC) The return value of eval is a tibble that shows some summary statistics (e.g. mean, median) of the evaluation measures across bootstrap samples, for each classification algorithm.
bests: best-performing algorithm for each bootstrapped replicate of the data, chosen by rank aggregation.
ensemble_algs: tallies the frequencies in bests, returning the top algorithms chosen.
ensemble: list of model fits for each of the algorithms in ensemble_algs, fit on the full data.

Details

Training sets are bootstrap replicates of the original data sampled with replacement. Test sets comprise of all remaining samples left out from each training set, also called Out-Of-Bag samples. This framework uses the 0.632 bootstrap rule for large n.

An ensemble classifier is constructed using Rank Aggregation across multiple evaluation measures such as precision, recall, F1-score, and Matthew's Correlation Coefficient (MCC).

Algorithms

The classification algorithms currently supported are:

Prediction Analysis for Microarrays ("pam")
Support Vector Machines ("svm")
Random Forests ("rf")
Linear Discriminant Analysis ("lda")
Shrinkage Linear Discriminant Analysis ("slda")
Shrinkage Diagonal Discriminant Analysis ("sdda")
Multinomial Logistic Regression using
- Generalized Linear Model with no penalization ("mlr_glm")
- GLM with LASSO penalty ("mlr_lasso")
- GLM with ridge penalty ("mlr_ridge")
- Neural Networks ("mlr_nnet")
Neural Networks ("nnet")
Naive Bayes ("nbayes")
Adaptive Boosting ("adaboost")
AdaBoost.M1 ("adaboost_m1")
Extreme Gradient Boosting ("xgboost")
K-Nearest Neighbours ("knn")

Author

Derek Chiu

Examples

if (FALSE) {
data(hgsc)
class <- attr(hgsc, "class.true")
sl_result <- splendid(hgsc, class, n = 2, algorithms = c("lda", "xgboost"))
}