282 research outputs found
Accuracy Measures for the Comparison of Classifiers
The selection of the best classification algorithm for a given dataset is a
very widespread problem. It is also a complex one, in the sense it requires to
make several important methodological choices. Among them, in this work we
focus on the measure used to assess the classification performance and rank the
algorithms. We present the most popular measures and discuss their properties.
Despite the numerous measures proposed over the years, many of them turn out to
be equivalent in this specific case, to have interpretation problems, or to be
unsuitable for our purpose. Consequently, classic overall success rate or
marginal rates should be preferred for this specific task.Comment: The 5th International Conference on Information Technology, amman :
Jordanie (2011
Evaluation of Performance Measures for Classifiers Comparison
The selection of the best classification algorithm for a given dataset is a
very widespread problem, occuring each time one has to choose a classifier to
solve a real-world problem. It is also a complex task with many important
methodological decisions to make. Among those, one of the most crucial is the
choice of an appropriate measure in order to properly assess the classification
performance and rank the algorithms. In this article, we focus on this specific
task. We present the most popular measures and compare their behavior through
discrimination plots. We then discuss their properties from a more theoretical
perspective. It turns out several of them are equivalent for classifiers
comparison purposes. Futhermore. they can also lead to interpretation problems.
Among the numerous measures proposed over the years, it appears that the
classical overall success rate and marginal rates are the more suitable for
classifier comparison task
Fusing image representations for classification using support vector machines
In order to improve classification accuracy different image representations
are usually combined. This can be done by using two different fusing schemes.
In feature level fusion schemes, image representations are combined before the
classification process. In classifier fusion, the decisions taken separately
based on individual representations are fused to make a decision. In this paper
the main methods derived for both strategies are evaluated. Our experimental
results show that classifier fusion performs better. Specifically Bayes belief
integration is the best performing strategy for image classification task.Comment: Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th
International Conference, Wellington : Nouvelle-Z\'elande (2009
Overlapping Community Structure in Co-authorship Networks: a Case Study
Community structure is one of the key properties of real-world complex
networks. It plays a crucial role in their behaviors and topology. While an
important work has been done on the issue of community detection, very little
attention has been devoted to the analysis of the community structure. In this
paper, we present an extensive investigation of the overlapping community
network deduced from a large-scale co-authorship network. The nodes of the
overlapping community network represent the functional communities of the
co-authorship network, and the links account for the fact that communities
share some nodes in the co-authorship network. The comparative evaluation of
the topological properties of these two networks shows that they share similar
topological properties. These results are very interesting. Indeed, the network
of communities seems to be a good representative of the original co-authorship
network. With its smaller size, it may be more practical in order to realize
various analyses that cannot be performed easily in large-scale real-world
networks.Comment: 2014 7th International Conference on u- and e- Service, Science and
Technolog
Community-based Immunization Strategies for Epidemic Control
Understanding the epidemic dynamics, and finding out efficient techniques to
control it, is a challenging issue. A lot of research has been done on targeted
immunization strategies, exploiting various global network topological
properties. However, in practice, information about the global structure of the
contact network may not be available. Therefore, immunization strategies that
can deal with a limited knowledge of the network structure are required. In
this paper, we propose targeted immunization strategies that require
information only at the community level. Results of our investigations on the
SIR epidemiological model, using a realistic synthetic benchmark with
controlled community structure, show that the community structure plays an
important role in the epidemic dynamics. An extensive comparative evaluation
demonstrates that the proposed strategies are as efficient as the most
influential global centrality based immunization strategies, despite the fact
that they use a limited amount of information. Furthermore, they outperform
alternative local strategies, which are agnostic about the network structure,
and make decisions based on random walks.Comment: 6 pages, 7 figure
Centrality Measures for Networks with Community Structure
Understanding the network structure, and finding out the influential nodes is
a challenging issue in the large networks. Identifying the most influential
nodes in the network can be useful in many applications like immunization of
nodes in case of epidemic spreading, during intentional attacks on complex
networks. A lot of research is done to devise centrality measures which could
efficiently identify the most influential nodes in the network. There are two
major approaches to the problem: On one hand, deterministic strategies that
exploit knowledge about the overall network topology in order to find the
influential nodes, while on the other end, random strategies are completely
agnostic about the network structure. Centrality measures that can deal with a
limited knowledge of the network structure are required. Indeed, in practice,
information about the global structure of the overall network is rarely
available or hard to acquire. Even if available, the structure of the network
might be too large that it is too much computationally expensive to calculate
global centrality measures. To that end, a centrality measure is proposed that
requires information only at the community level to identify the influential
nodes in the network. Indeed, most of the real-world networks exhibit a
community structure that can be exploited efficiently to discover the
influential nodes. We performed a comparative evaluation of prominent global
deterministic strategies together with stochastic strategies with an available
and the proposed deterministic community-based strategy. Effectiveness of the
proposed method is evaluated by performing experiments on synthetic and
real-world networks with community structure in the case of immunization of
nodes for epidemic control.Comment: 30 pages, 4 figures. Accepted for publication in Physica A. arXiv
admin note: text overlap with arXiv:1411.627
Comparative Evaluation of Community Detection Algorithms: A Topological Approach
Community detection is one of the most active fields in complex networks
analysis, due to its potential value in practical applications. Many works
inspired by different paradigms are devoted to the development of algorithmic
solutions allowing to reveal the network structure in such cohesive subgroups.
Comparative studies reported in the literature usually rely on a performance
measure considering the community structure as a partition (Rand Index,
Normalized Mutual information, etc.). However, this type of comparison neglects
the topological properties of the communities. In this article, we present a
comprehensive comparative study of a representative set of community detection
methods, in which we adopt both types of evaluation. Community-oriented
topological measures are used to qualify the communities and evaluate their
deviation from the reference structure. In order to mimic real-world systems,
we use artificially generated realistic networks. It turns out there is no
equivalence between both approaches: a high performance does not necessarily
correspond to correct topological properties, and vice-versa. They can
therefore be considered as complementary, and we recommend applying both of
them in order to perform a complete and accurate assessment
Towards realistic artificial benchmark for community detection algorithms evaluation
Assessing the partitioning performance of community detection algorithms is
one of the most important issues in complex network analysis. Artificially
generated networks are often used as benchmarks for this purpose. However,
previous studies showed their level of realism have a significant effect on the
algorithms performance. In this study, we adopt a thorough experimental
approach to tackle this problem and investigate this effect. To assess the
level of realism, we use consensual network topological properties. Based on
the LFR method, the most realistic generative method to date, we propose two
alternative random models to replace the Configuration Model originally used in
this algorithm, in order to increase its realism. Experimental results show
both modifications allow generating collections of community-structured
artificial networks whose topological properties are closer to those
encountered in real-world networks. Moreover, the results obtained with eleven
popular community identification algorithms on these benchmarks show their
performance decrease on more realistic networks
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