57,678 research outputs found
Analysis of Feature Rankings for Classification
Different ways of contrast generated rankings by feature selection algorithms are presented in this paper, showing several possible interpretations, depending on the given approach to each study. We begin from the premise of no existence of only one ideal subset for all cases. The purpose of these kinds of algorithms is to reduce the data set to each first attributes without losing prediction against the original data set. In this paper we propose a method, feature–ranking performance, to compare different feature–ranking methods, based on the Area Under Feature Ranking Classification Performance Curve (AURC). Conclusions and trends taken from this paper propose support for the performance of learning tasks, where some ranking algorithms studied here operate
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
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Exploring signature multiplicity in microarray data using ensembles of randomized trees
A challenging and novel direction for feature selection research
in computational biology is the analysis of signature multiplicity. In this work, we propose to investigate the eect of signature multiplicity on feature importance scores derived from tree-based ensemble methods. We show that looking at individual tree rankings in an ensemble could highlight the existence of multiple signatures and we propose a simple
post-processing method based on clustering that can return smaller signatures with better predictive performance than signatures derived from the global tree ranking at almost no additional cost
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