8,567 research outputs found
Robust Decision Trees Against Adversarial Examples
Although adversarial examples and model robustness have been extensively
studied in the context of linear models and neural networks, research on this
issue in tree-based models and how to make tree-based models robust against
adversarial examples is still limited. In this paper, we show that tree based
models are also vulnerable to adversarial examples and develop a novel
algorithm to learn robust trees. At its core, our method aims to optimize the
performance under the worst-case perturbation of input features, which leads to
a max-min saddle point problem. Incorporating this saddle point objective into
the decision tree building procedure is non-trivial due to the discrete nature
of trees --- a naive approach to finding the best split according to this
saddle point objective will take exponential time. To make our approach
practical and scalable, we propose efficient tree building algorithms by
approximating the inner minimizer in this saddle point problem, and present
efficient implementations for classical information gain based trees as well as
state-of-the-art tree boosting models such as XGBoost. Experimental results on
real world datasets demonstrate that the proposed algorithms can substantially
improve the robustness of tree-based models against adversarial examples
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
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