60,432 research outputs found
Test-cost sensitive classification based on conditioned loss functions
We report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this approach, we propose to use a new Bayesian decision theoretical framework in which the loss is conditioned with the current decision and the expected decisions after additional features are extracted as well as the consistency among the current and expected decisions. This approach allows us to force the feature extraction for samples for which the current and expected decisions are inconsistent. On the other hand, it forces not to extract any features in the case of consistency, leading to less costly but equally accurate decisions. In this work, we apply this approach to a medical diagnosis problem and demonstrate that it reduces the overall feature extraction cost up to 47.61 percent without decreasing the accuracy. © Springer-Verlag Berlin Heidelberg 2007
Certifying and removing disparate impact
What does it mean for an algorithm to be biased? In U.S. law, unintentional
bias is encoded via disparate impact, which occurs when a selection process has
widely different outcomes for different groups, even as it appears to be
neutral. This legal determination hinges on a definition of a protected class
(ethnicity, gender, religious practice) and an explicit description of the
process.
When the process is implemented using computers, determining disparate impact
(and hence bias) is harder. It might not be possible to disclose the process.
In addition, even if the process is open, it might be hard to elucidate in a
legal setting how the algorithm makes its decisions. Instead of requiring
access to the algorithm, we propose making inferences based on the data the
algorithm uses.
We make four contributions to this problem. First, we link the legal notion
of disparate impact to a measure of classification accuracy that while known,
has received relatively little attention. Second, we propose a test for
disparate impact based on analyzing the information leakage of the protected
class from the other data attributes. Third, we describe methods by which data
might be made unbiased. Finally, we present empirical evidence supporting the
effectiveness of our test for disparate impact and our approach for both
masking bias and preserving relevant information in the data. Interestingly,
our approach resembles some actual selection practices that have recently
received legal scrutiny.Comment: Extended version of paper accepted at 2015 ACM SIGKDD Conference on
Knowledge Discovery and Data Minin
The Power of Asymmetry in Binary Hashing
When approximating binary similarity using the hamming distance between short
binary hashes, we show that even if the similarity is symmetric, we can have
shorter and more accurate hashes by using two distinct code maps. I.e. by
approximating the similarity between and as the hamming distance
between and , for two distinct binary codes , rather than as
the hamming distance between and .Comment: Accepted to NIPS 2013, 9 pages, 5 figure
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