5,818 research outputs found
A Confidence-Based Approach for Balancing Fairness and Accuracy
We study three classical machine learning algorithms in the context of
algorithmic fairness: adaptive boosting, support vector machines, and logistic
regression. Our goal is to maintain the high accuracy of these learning
algorithms while reducing the degree to which they discriminate against
individuals because of their membership in a protected group.
Our first contribution is a method for achieving fairness by shifting the
decision boundary for the protected group. The method is based on the theory of
margins for boosting. Our method performs comparably to or outperforms previous
algorithms in the fairness literature in terms of accuracy and low
discrimination, while simultaneously allowing for a fast and transparent
quantification of the trade-off between bias and error.
Our second contribution addresses the shortcomings of the bias-error
trade-off studied in most of the algorithmic fairness literature. We
demonstrate that even hopelessly naive modifications of a biased algorithm,
which cannot be reasonably said to be fair, can still achieve low bias and high
accuracy. To help to distinguish between these naive algorithms and more
sensible algorithms we propose a new measure of fairness, called resilience to
random bias (RRB). We demonstrate that RRB distinguishes well between our naive
and sensible fairness algorithms. RRB together with bias and accuracy provides
a more complete picture of the fairness of an algorithm
Automated reliability assessment for spectroscopic redshift measurements
We present a new approach to automate the spectroscopic redshift reliability
assessment based on machine learning (ML) and characteristics of the redshift
probability density function (PDF).
We propose to rephrase the spectroscopic redshift estimation into a Bayesian
framework, in order to incorporate all sources of information and uncertainties
related to the redshift estimation process, and produce a redshift posterior
PDF that will be the starting-point for ML algorithms to provide an automated
assessment of a redshift reliability.
As a use case, public data from the VIMOS VLT Deep Survey is exploited to
present and test this new methodology. We first tried to reproduce the existing
reliability flags using supervised classification to describe different types
of redshift PDFs, but due to the subjective definition of these flags, soon
opted for a new homogeneous partitioning of the data into distinct clusters via
unsupervised classification. After assessing the accuracy of the new clusters
via resubstitution and test predictions, unlabelled data from preliminary mock
simulations for the Euclid space mission are projected into this mapping to
predict their redshift reliability labels.Comment: Submitted on 02 June 2017 (v1). Revised on 08 September 2017 (v2).
Latest version 28 September 2017 (this version v3
Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications
Business optimization is becoming increasingly important because all business
activities aim to maximize the profit and performance of products and services,
under limited resources and appropriate constraints. Recent developments in
support vector machine and metaheuristics show many advantages of these
techniques. In particular, particle swarm optimization is now widely used in
solving tough optimization problems. In this paper, we use a combination of a
recently developed Accelerated PSO and a nonlinear support vector machine to
form a framework for solving business optimization problems. We first apply the
proposed APSO-SVM to production optimization, and then use it for income
prediction and project scheduling. We also carry out some parametric studies
and discuss the advantages of the proposed metaheuristic SVM.Comment: 12 page
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