6,541 research outputs found
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics
The combination of multiple classifiers using ensemble methods is
increasingly important for making progress in a variety of difficult prediction
problems. We present a comparative analysis of several ensemble methods through
two case studies in genomics, namely the prediction of genetic interactions and
protein functions, to demonstrate their efficacy on real-world datasets and
draw useful conclusions about their behavior. These methods include simple
aggregation, meta-learning, cluster-based meta-learning, and ensemble selection
using heterogeneous classifiers trained on resampled data to improve the
diversity of their predictions. We present a detailed analysis of these methods
across 4 genomics datasets and find the best of these methods offer
statistically significant improvements over the state of the art in their
respective domains. In addition, we establish a novel connection between
ensemble selection and meta-learning, demonstrating how both of these disparate
methods establish a balance between ensemble diversity and performance.Comment: 10 pages, 3 figures, 8 tables, to appear in Proceedings of the 2013
International Conference on Data Minin
Visual Integration of Data and Model Space in Ensemble Learning
Ensembles of classifier models typically deliver superior performance and can
outperform single classifier models given a dataset and classification task at
hand. However, the gain in performance comes together with the lack in
comprehensibility, posing a challenge to understand how each model affects the
classification outputs and where the errors come from. We propose a tight
visual integration of the data and the model space for exploring and combining
classifier models. We introduce a workflow that builds upon the visual
integration and enables the effective exploration of classification outputs and
models. We then present a use case in which we start with an ensemble
automatically selected by a standard ensemble selection algorithm, and show how
we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture
Ensemble Learning for Free with Evolutionary Algorithms ?
Evolutionary Learning proceeds by evolving a population of classifiers, from
which it generally returns (with some notable exceptions) the single
best-of-run classifier as final result. In the meanwhile, Ensemble Learning,
one of the most efficient approaches in supervised Machine Learning for the
last decade, proceeds by building a population of diverse classifiers. Ensemble
Learning with Evolutionary Computation thus receives increasing attention. The
Evolutionary Ensemble Learning (EEL) approach presented in this paper features
two contributions. First, a new fitness function, inspired by co-evolution and
enforcing the classifier diversity, is presented. Further, a new selection
criterion based on the classification margin is proposed. This criterion is
used to extract the classifier ensemble from the final population only
(Off-line) or incrementally along evolution (On-line). Experiments on a set of
benchmark problems show that Off-line outperforms single-hypothesis
evolutionary learning and state-of-art Boosting and generates smaller
classifier ensembles
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