errorevol_ranking_vector_IW {adabag}

Calculate the error evolution and final predictions of an item-weighted ensemble for rankings

Description

This function calculates the error evolution and final predictions of an item-weigthed ensemble method for ranking data (Albano et al., 2023).

Usage

  errorevol_ranking_vector_IW(object, newdata, iw, squared = FALSE)

Arguments

object

an object of class 'bagging' or 'boosting' generated by the Ensemble_ranking_IW function.

newdata

a data frame that can be the same as the one used in the object or a new one. Continuous variables are allowed, while the dummy coding should be used for categorical variables. It must be the output of the prep_data function.

iw

a weighting vector or matrix. For coherence, iw should be the same vector/matrix used in Ensemble_ranking_IW(...).

squared

logical value indicating whether squared weighting should be used in the final prediction. Default is FALSE. When set to TRUE, it corresponds to utilizing the AdaBoost.R.M3 algorithm defined by Albano et al. (2023).

Details

This function computes the error and final predictions for a boosting or bagging ranking model using item weighting.

Value

An object of class 'errorevol'. It has two components:

error

a vector with the error values at each ensemble iteration

final_prediction

a data frame of final predictions for each observation in newdata.

References

Albano, A., Sciandra, M., and Plaia, A. (2023): "A weighted distance-based approach with boosted decision trees for label ranking." Expert Systems with Applications.

Alfaro, E., Gamez, M., and Garcia, N. (2013): "adabag: An R Package for Classification with Boosting and Bagging." Journal of Statistical Software, Vol. 54, 2, pp. 1–35.

Breiman, L. (1998): "Arcing classifiers." The Annals of Statistics, Vol. 26, 3, pp. 801–849.

D'Ambrosio, A.[aut, cre], Amodio, S. [ctb], Mazzeo, G. [ctb], Albano, A. [ctb], Plaia, A. [ctb] (2023). ConsRank: Compute the Median Ranking(s) According to the Kemeny's Axiomatic Approach. R package version 2.1.3, https://cran.r-project.org/package=ConsRank.

Freund, Y., and Schapire, R.E. (1996): "Experiments with a new boosting algorithm." In Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156, Morgan Kaufmann.

Plaia, A., Buscemi, S., Furnkranz, J., and Mencıa, E.L. (2021): "Comparing boosting and bagging for decision trees of rankings." Journal of Classification, pages 1–22.

Zhu, J., Zou, H., Rosset, S., and Hastie, T. (2009): "Multi-class AdaBoost." Statistics and Its Interface, 2, pp. 349–360.

Examples

## Not run: 
  # Load simulated ranking data
  data(simulatedRankingData)
  x <- simulatedRankingData$x
  y <- simulatedRankingData$y

  # Prepare the data with item weights
  dati <- prep_data(y, x, iw = c(2, 5, 5, 2))

  # Divide the data into training and test sets
  set.seed(12345)
  samp <- sample(nrow(dati))
  l <- length(dati[, 1])
  sub <- sample(1:l, 2 * l / 3)
  data_sub1 <- dati[sub, ]
  data_test1 <- dati[-sub, ]

  # Apply ensemble ranking with AdaBoost.M1
  boosting_1 <- Ensemble_ranking_IW(
    Label ~ .,
    data = data_sub1,
    iw = c(2, 5, 5, 2),
    mfinal = 3,
    coeflearn = "Breiman",
    control = rpart.control(maxdepth = 4, cp = -1),
    algo = "boosting",
    bin = FALSE
  )

  # Evaluate the performance
  test_boosting1 <- errorevol_ranking_vector_IW(boosting_1, 
    newdata = data_test1, iw=c(2,5,5,2), squared = FALSE)
  test_boosting1.1 <- errorevol_ranking_vector_IW(boosting_1, 
    newdata = data_sub1, iw=c(2,5,5,2), squared = FALSE)

  # Plot the error evolution
  plot.errorevol(test_boosting1, test_boosting1.1)
  
## End(Not run)