12,153 research outputs found
Dissimilarity-based Ensembles for Multiple Instance Learning
In multiple instance learning, objects are sets (bags) of feature vectors
(instances) rather than individual feature vectors. In this paper we address
the problem of how these bags can best be represented. Two standard approaches
are to use (dis)similarities between bags and prototype bags, or between bags
and prototype instances. The first approach results in a relatively
low-dimensional representation determined by the number of training bags, while
the second approach results in a relatively high-dimensional representation,
determined by the total number of instances in the training set. In this paper
a third, intermediate approach is proposed, which links the two approaches and
combines their strengths. Our classifier is inspired by a random subspace
ensemble, and considers subspaces of the dissimilarity space, defined by
subsets of instances, as prototypes. We provide guidelines for using such an
ensemble, and show state-of-the-art performances on a range of multiple
instance learning problems.Comment: Submitted to IEEE Transactions on Neural Networks and Learning
Systems, Special Issue on Learning in Non-(geo)metric Space
EC3: Combining Clustering and Classification for Ensemble Learning
Classification and clustering algorithms have been proved to be successful
individually in different contexts. Both of them have their own advantages and
limitations. For instance, although classification algorithms are more powerful
than clustering methods in predicting class labels of objects, they do not
perform well when there is a lack of sufficient manually labeled reliable data.
On the other hand, although clustering algorithms do not produce label
information for objects, they provide supplementary constraints (e.g., if two
objects are clustered together, it is more likely that the same label is
assigned to both of them) that one can leverage for label prediction of a set
of unknown objects. Therefore, systematic utilization of both these types of
algorithms together can lead to better prediction performance. In this paper,
We propose a novel algorithm, called EC3 that merges classification and
clustering together in order to support both binary and multi-class
classification. EC3 is based on a principled combination of multiple
classification and multiple clustering methods using an optimization function.
We theoretically show the convexity and optimality of the problem and solve it
by block coordinate descent method. We additionally propose iEC3, a variant of
EC3 that handles imbalanced training data. We perform an extensive experimental
analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known
standalone classifiers, 5 ensemble classifiers, and 2 existing methods that
merge classification and clustering) on 13 standard benchmark datasets. We show
that our methods outperform other baselines for every single dataset, achieving
at most 10% higher AUC. Moreover our methods are faster (1.21 times faster than
the best baseline), more resilient to noise and class imbalance than the best
baseline method.Comment: 14 pages, 7 figures, 11 table
A novel two stage scheme utilizing the test set for model selection in text classification
Text classification is a natural application domain for semi-supervised learning, as labeling documents is expensive, but on the other hand usually an abundance of unlabeled documents is available. We describe a novel simple two stage scheme based on dagging which allows for utilizing the test set in model selection. The dagging ensemble can also be used by itself instead of the original classifier. We evaluate the performance of a meta classifier choosing between various base learners and their respective dagging ensembles. The selection process seems to perform robustly especially for small percentages of available labels for training
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
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