14,405 research outputs found
Evaluation Measures for Hierarchical Classification: a unified view and novel approaches
Hierarchical classification addresses the problem of classifying items into a
hierarchy of classes. An important issue in hierarchical classification is the
evaluation of different classification algorithms, which is complicated by the
hierarchical relations among the classes. Several evaluation measures have been
proposed for hierarchical classification using the hierarchy in different ways.
This paper studies the problem of evaluation in hierarchical classification by
analyzing and abstracting the key components of the existing performance
measures. It also proposes two alternative generic views of hierarchical
evaluation and introduces two corresponding novel measures. The proposed
measures, along with the state-of-the art ones, are empirically tested on three
large datasets from the domain of text classification. The empirical results
illustrate the undesirable behavior of existing approaches and how the proposed
methods overcome most of these methods across a range of cases.Comment: Submitted to journa
On the Role of Social Identity and Cohesion in Characterizing Online Social Communities
Two prevailing theories for explaining social group or community structure
are cohesion and identity. The social cohesion approach posits that social
groups arise out of an aggregation of individuals that have mutual
interpersonal attraction as they share common characteristics. These
characteristics can range from common interests to kinship ties and from social
values to ethnic backgrounds. In contrast, the social identity approach posits
that an individual is likely to join a group based on an intrinsic
self-evaluation at a cognitive or perceptual level. In other words group
members typically share an awareness of a common category membership.
In this work we seek to understand the role of these two contrasting theories
in explaining the behavior and stability of social communities in Twitter. A
specific focal point of our work is to understand the role of these theories in
disparate contexts ranging from disaster response to socio-political activism.
We extract social identity and social cohesion features-of-interest for large
scale datasets of five real-world events and examine the effectiveness of such
features in capturing behavioral characteristics and the stability of groups.
We also propose a novel measure of social group sustainability based on the
divergence in group discussion. Our main findings are: 1) Sharing of social
identities (especially physical location) among group members has a positive
impact on group sustainability, 2) Structural cohesion (represented by high
group density and low average shortest path length) is a strong indicator of
group sustainability, and 3) Event characteristics play a role in shaping group
sustainability, as social groups in transient events behave differently from
groups in events that last longer
Networking - A Statistical Physics Perspective
Efficient networking has a substantial economic and societal impact in a
broad range of areas including transportation systems, wired and wireless
communications and a range of Internet applications. As transportation and
communication networks become increasingly more complex, the ever increasing
demand for congestion control, higher traffic capacity, quality of service,
robustness and reduced energy consumption require new tools and methods to meet
these conflicting requirements. The new methodology should serve for gaining
better understanding of the properties of networking systems at the macroscopic
level, as well as for the development of new principled optimization and
management algorithms at the microscopic level. Methods of statistical physics
seem best placed to provide new approaches as they have been developed
specifically to deal with non-linear large scale systems. This paper aims at
presenting an overview of tools and methods that have been developed within the
statistical physics community and that can be readily applied to address the
emerging problems in networking. These include diffusion processes, methods
from disordered systems and polymer physics, probabilistic inference, which
have direct relevance to network routing, file and frequency distribution, the
exploration of network structures and vulnerability, and various other
practical networking applications.Comment: (Review article) 71 pages, 14 figure
Approximate Capacities of Two-Dimensional Codes by Spatial Mixing
We apply several state-of-the-art techniques developed in recent advances of
counting algorithms and statistical physics to study the spatial mixing
property of the two-dimensional codes arising from local hard (independent set)
constraints, including: hard-square, hard-hexagon, read/write isolated memory
(RWIM), and non-attacking kings (NAK). For these constraints, the strong
spatial mixing would imply the existence of polynomial-time approximation
scheme (PTAS) for computing the capacity. It was previously known for the
hard-square constraint the existence of strong spatial mixing and PTAS. We show
the existence of strong spatial mixing for hard-hexagon and RWIM constraints by
establishing the strong spatial mixing along self-avoiding walks, and
consequently we give PTAS for computing the capacities of these codes. We also
show that for the NAK constraint, the strong spatial mixing does not hold along
self-avoiding walks
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