4,874 research outputs found
New multicategory boosting algorithms based on multicategory Fisher-consistent losses
Fisher-consistent loss functions play a fundamental role in the construction
of successful binary margin-based classifiers. In this paper we establish the
Fisher-consistency condition for multicategory classification problems. Our
approach uses the margin vector concept which can be regarded as a
multicategory generalization of the binary margin. We characterize a wide class
of smooth convex loss functions that are Fisher-consistent for multicategory
classification. We then consider using the margin-vector-based loss functions
to derive multicategory boosting algorithms. In particular, we derive two new
multicategory boosting algorithms by using the exponential and logistic
regression losses.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS198 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Algorithm selection on data streams
We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier performs best on the entire stream. This yields promising results and interesting patterns. In a second experiment, we build a meta-classifier that predicts, based on measurable data characteristics in a window of the data stream, the best classifier for the next window. The results show that this meta-algorithm is very competitive with state of the art ensembles, such as OzaBag, OzaBoost and Leveraged Bagging. The results of all experiments are made publicly available in an online experiment database, for the purpose of verifiability, reproducibility and generalizability
- …