The Strength of Weak Learnability
Machine Learning
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Prediction games and arcing algorithms
Neural Computation
Genetic Programming Prediction of Stock Prices
Computational Economics
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Applying Boosting Techniques to Genetic Programming
Selected Papers from the 5th European Conference on Artificial Evolution
Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Rule-based machine learning methods for functional prediction
Journal of Artificial Intelligence Research
On the effect of calibration in classifier combination
Applied Intelligence
Hi-index | 0.00 |
This paper explores the Genetic Programming and Boosting technique to obtain an ensemble of regressors and proposes a new formula for the updating of weights, as well as for the final hypothesis. Differently from studies found in the literature, in this paper we investigate the use of the correlation metric as an additional factor for the error metric. This new approach, called Boosting using Correlation Coefficients (BCC) has been empirically obtained after trying to improve the results of the other methods. To validate this method, we conducted two groups of experiments. In the first group, we explore the BCC for time series forecasting, in academic series and in a widespread Monte Carlo simulation covering the entire ARMA spectrum. The Genetic Programming (GP) is used as a base learner and the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using traditional boosting and the traditional statistical methodology (ARMA). The second group of experiments aims at evaluating the proposed method on multivariate regression problems by choosing Cart (Classification and Regression Tree) as the base learner.