25,406 research outputs found
Multilabel Consensus Classification
In the era of big data, a large amount of noisy and incomplete data can be
collected from multiple sources for prediction tasks. Combining multiple models
or data sources helps to counteract the effects of low data quality and the
bias of any single model or data source, and thus can improve the robustness
and the performance of predictive models. Out of privacy, storage and bandwidth
considerations, in certain circumstances one has to combine the predictions
from multiple models or data sources to obtain the final predictions without
accessing the raw data. Consensus-based prediction combination algorithms are
effective for such situations. However, current research on prediction
combination focuses on the single label setting, where an instance can have one
and only one label. Nonetheless, data nowadays are usually multilabeled, such
that more than one label have to be predicted at the same time. Direct
applications of existing prediction combination methods to multilabel settings
can lead to degenerated performance. In this paper, we address the challenges
of combining predictions from multiple multilabel classifiers and propose two
novel algorithms, MLCM-r (MultiLabel Consensus Maximization for ranking) and
MLCM-a (MLCM for microAUC). These algorithms can capture label correlations
that are common in multilabel classifications, and optimize corresponding
performance metrics. Experimental results on popular multilabel classification
tasks verify the theoretical analysis and effectiveness of the proposed
methods
Deciphering a novel image cipher based on mixed transformed Logistic maps
Since John von Neumann suggested utilizing Logistic map as a random number
generator in 1947, a great number of encryption schemes based on Logistic map
and/or its variants have been proposed. This paper re-evaluates the security of
an image cipher based on transformed logistic maps and proves that the image
cipher can be deciphered efficiently under two different conditions: 1) two
pairs of known plain-images and the corresponding cipher-images with
computational complexity of ; 2) two pairs of chosen plain-images
and the corresponding cipher-images with computational complexity of ,
where is the number of pixels in the plain-image. In contrast, the required
condition in the previous deciphering method is eighty-seven pairs of chosen
plain-images and the corresponding cipher-images with computational complexity
of . In addition, three other security flaws existing in most
Logistic-map-based ciphers are also reported.Comment: 10 pages, 2 figure
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