23,665 research outputs found
RankMerging: A supervised learning-to-rank framework to predict links in large social network
Uncovering unknown or missing links in social networks is a difficult task
because of their sparsity and because links may represent different types of
relationships, characterized by different structural patterns. In this paper,
we define a simple yet efficient supervised learning-to-rank framework, called
RankMerging, which aims at combining information provided by various
unsupervised rankings. We illustrate our method on three different kinds of
social networks and show that it substantially improves the performances of
unsupervised metrics of ranking. We also compare it to other combination
strategies based on standard methods. Finally, we explore various aspects of
RankMerging, such as feature selection and parameter estimation and discuss its
area of relevance: the prediction of an adjustable number of links on large
networks.Comment: 43 pages, published in Machine Learning Journa
Weighted Heuristic Ensemble of Filters
Feature selection has become increasingly important in data mining in recent years due to the rapid increase in the dimensionality of big data. However, the reliability and consistency of feature selection methods (filters) vary considerably on different data and no single filter performs consistently well under various conditions. Therefore, feature selection ensemble has been investigated recently to provide more reliable and effective results than any individual one but all the existing feature selection ensemble treat the feature selection methods equally regardless of their performance. In this paper, we present a novel framework which applies weighted feature selection ensemble through proposing a systemic way of adding different weights to the feature selection methods-filters. Also, we investigate how to determine the appropriate weight for each filter in an ensemble. Experiments based on ten benchmark datasets show that theoretically and intuitively adding more weight to ‘good filters’ should lead to better results but in reality it is very uncertain. This assumption was found to be correct for some examples in our experiment. However, for other situations, filters which had been assumed to perform well showed bad performance leading to even worse results. Therefore adding weight to filters might not achieve much in accuracy terms, in addition to increasing complexity, time consumption and clearly decreasing the stability
Operationalizing Individual Fairness with Pairwise Fair Representations
We revisit the notion of individual fairness proposed by Dwork et al. A
central challenge in operationalizing their approach is the difficulty in
eliciting a human specification of a similarity metric. In this paper, we
propose an operationalization of individual fairness that does not rely on a
human specification of a distance metric. Instead, we propose novel approaches
to elicit and leverage side-information on equally deserving individuals to
counter subordination between social groups. We model this knowledge as a
fairness graph, and learn a unified Pairwise Fair Representation (PFR) of the
data that captures both data-driven similarity between individuals and the
pairwise side-information in fairness graph. We elicit fairness judgments from
a variety of sources, including human judgments for two real-world datasets on
recidivism prediction (COMPAS) and violent neighborhood prediction (Crime &
Communities). Our experiments show that the PFR model for operationalizing
individual fairness is practically viable.Comment: To be published in the proceedings of the VLDB Endowment, Vol. 13,
Issue.
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