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Fisher networks: A principled approach to retrieval-based classification

Ruiz, H (2013) Fisher networks: A principled approach to retrieval-based classification. Doctoral thesis, Liverpool John Moores University.

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Abstract

Due to the technological advances in the acquisition and processing of information, current data
mining applications involve databases of sizes that would be unthinkable just two decades ago.
However, real-word datasets are often riddled with irrelevant variables that not only do not
generate any meaningful information about the process of interest, but may also obstruct the
contribution of the truly informative data features. Taking into consideration the relevance of
the di�erent measures available can make the di�erence between reaching an accurate re
ection
of the underlying truth and obtaining misleading results that cause the drawing of erroneous
conclusions.

Another important consideration in data analysis is the interpretability of the models used to �t
the data. It is clear that performance must be a key aspect in deciding which methodology to
use, but it should not be the only one. Models with an obscure internal operation see their practical
usefulness e�ectively diminished by the di�culty to understand the reasoning behind their
inferences, which makes them less appealing to users that are not familiar with their theoretical
basis.

This thesis proposes a novel framework for the visualisation and categorisation of data in classi�-
cation contexts that tackles the two issues discussed above and provides an informative output of
intuitive interpretation. The system is based on a Fisher information metric that automatically
�lters the contribution of variables depending on their relevance with respect to the classi�cation
problem at hand, measured by their in
uence on the posterior class probabilities.

Fisher distances can then be used to calculate rigorous problem-speci�c similarity measures, which
can be grouped into a pairwise adjacency matrix, thus de�ning a network. Following this novel
construction process results in a principled visualisation of the data organised in communities
that highlights the structure of the underlying class membership probabilities. Furthermore, the
relational nature of the network can be used to reproduce the probabilistic predictions of the
original estimates in a case-based approach, making them explainable by means of known cases
in the dataset.

The potential applications and usefulness of the framework are illustrated using several real-world
datasets, giving examples of the typical output that the end user receives and how they can use
it to learn more about the cases of interest as well as about the dataset as a whole.

Item Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Applied Mathematics
Date Deposited: 27 Oct 2016 13:20
Last Modified: 22 Nov 2016 09:29
Supervisors: Lisboa, P and Jarman, I and Martin-Guerrero, JD
URI: http://researchonline.ljmu.ac.uk/id/eprint/4371

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