Machine Learning for Survival Analysis: A Survey <s> Advanced Machine Learning Approaches <s> Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression. <s> BIB001 </s> Machine Learning for Survival Analysis: A Survey <s> Advanced Machine Learning Approaches <s> Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy. <s> BIB002
Over the past few years, a number of advanced machine learning methods have been developed to deal with and make predictions based on censored data. These methods offer unique advantages for survival data compared to other methods described so far. (Dietterich 2000) generate a committee of classifiers and then predict the class labels for new data points as they arrive by taking a weighted vote among the prediction results from all these classifiers. It is often possible to construct good ensembles and obtain a better approximation of the unknown function by varying the initial points, especially in the presence of insufficient data. To overcome the instability of a single method, bagging BIB002 and random forests BIB001 are generally used to perform the ensemble-based model building. Such ensemble models have been successfully adapted to survival analysis whose time complexity mainly follows that of the base-learners.