TY - GEN
T1 - AR-Boost
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013
AU - Saha, Baidya Nath
AU - Kunapuli, Gautam
AU - Ray, Nilanjan
AU - Maldjian, Joseph A
AU - Natarajan, Sriraam
PY - 2013
Y1 - 2013
N2 - We introduce a novel, robust data-driven regularization strategy called Adaptive Regularized Boosting (AR-Boost), motivated by a desire to reduce overfitting. We replace AdaBoost's hard margin with a regularized soft margin that trades-off between a larger margin, at the expense of misclassification errors. Minimizing this regularized exponential loss results in a boosting algorithm that relaxes the weak learning assumption further: it can use classifiers with error greater than 1/2. This enables a natural extension to multiclass boosting, and further reduces overfitting in both the binary and multiclass cases. We derive bounds for training and generalization errors, and relate them to AdaBoost. Finally, we show empirical results on benchmark data that establish the robustness of our approach and improved performance overall.
AB - We introduce a novel, robust data-driven regularization strategy called Adaptive Regularized Boosting (AR-Boost), motivated by a desire to reduce overfitting. We replace AdaBoost's hard margin with a regularized soft margin that trades-off between a larger margin, at the expense of misclassification errors. Minimizing this regularized exponential loss results in a boosting algorithm that relaxes the weak learning assumption further: it can use classifiers with error greater than 1/2. This enables a natural extension to multiclass boosting, and further reduces overfitting in both the binary and multiclass cases. We derive bounds for training and generalization errors, and relate them to AdaBoost. Finally, we show empirical results on benchmark data that establish the robustness of our approach and improved performance overall.
UR - http://www.scopus.com/inward/record.url?scp=84886476301&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886476301&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40994-3_1
DO - 10.1007/978-3-642-40994-3_1
M3 - Conference contribution
AN - SCOPUS:84886476301
SN - 9783642409936
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 16
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Proceedings
Y2 - 23 September 2013 through 27 September 2013
ER -