DeepAI AI Chat
Log In Sign Up

Can Evolutionary Sampling Improve Bagged Ensembles?

by   Harsh Nisar, et al.

Perturb and Combine (P&C) group of methods generate multiple versions of the predictor by perturbing the training set or construction and then combining them into a single predictor (Breiman, 1996b). The motive is to improve the accuracy in unstable classification and regression methods. One of the most well known method in this group is Bagging. Arcing or Adaptive Resampling and Combining methods like AdaBoost are smarter variants of P&C methods. In this extended abstract, we lay the groundwork for a new family of methods under the P&C umbrella, known as Evolutionary Sampling (ES). We employ Evolutionary algorithms to suggest smarter sampling in both the feature space (sub-spaces) as well as training samples. We discuss multiple fitness functions to assess ensembles and empirically compare our performance against randomized sampling of training data and feature sub-spaces.


page 1

page 2

page 3


Group theory, group actions, evolutionary algorithms, and global optimization

In this paper we use group, action and orbit to understand how evolution...

Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation

Generative Adversarial Networks (GANs) are popular tools for generative ...

A Crossover That Matches Diverse Parents Together in Evolutionary Algorithms

Crossover and mutation are the two main operators that lead to new solut...

Haploid-Diploid Evolutionary Algorithms

This paper uses the recent idea that the fundamental haploid-diploid lif...

On the Robustness of Median Sampling in Noisy Evolutionary Optimization

In real-world optimization tasks, the objective (i.e., fitness) function...