17,739 research outputs found
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
Combination of linear classifiers using score function -- analysis of possible combination strategies
In this work, we addressed the issue of combining linear classifiers using
their score functions. The value of the scoring function depends on the
distance from the decision boundary. Two score functions have been tested and
four different combination strategies were investigated. During the
experimental study, the proposed approach was applied to the heterogeneous
ensemble and it was compared to two reference methods -- majority voting and
model averaging respectively. The comparison was made in terms of seven
different quality criteria. The result shows that combination strategies based
on simple average, and trimmed average are the best combination strategies of
the geometrical combination
Nearest Labelset Using Double Distances for Multi-label Classification
Multi-label classification is a type of supervised learning where an instance
may belong to multiple labels simultaneously. Predicting each label
independently has been criticized for not exploiting any correlation between
labels. In this paper we propose a novel approach, Nearest Labelset using
Double Distances (NLDD), that predicts the labelset observed in the training
data that minimizes a weighted sum of the distances in both the feature space
and the label space to the new instance. The weights specify the relative
tradeoff between the two distances. The weights are estimated from a binomial
regression of the number of misclassified labels as a function of the two
distances. Model parameters are estimated by maximum likelihood. NLDD only
considers labelsets observed in the training data, thus implicitly taking into
account label dependencies. Experiments on benchmark multi-label data sets show
that the proposed method on average outperforms other well-known approaches in
terms of Hamming loss, 0/1 loss, and multi-label accuracy and ranks second
after ECC on the F-measure
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