19 research outputs found
Enhancing multi-class classification in FARC-HD fuzzy classifier: on the synergy between n-dimensional overlap functions and decomposition strategies
There are many real-world classification problems involving multiple classes, e.g., in bioinformatics, computer vision or medicine. These problems are generally more difficult than their binary counterparts. In this scenario, decomposition strategies usually improve the performance of classifiers. Hence, in this paper we aim to improve the behaviour of FARC-HD fuzzy classifier in multi-class classification problems using decomposition strategies, and more specifically One-vs-One (OVO) and One-vs-All (OVA) strategies. However, when these strategies are applied on FARC-HD a problem emerges due to the low confidence values provided by the fuzzy reasoning method. This undesirable condition comes from the application of the product t-norm when computing the matching and association degrees, obtaining low values, which are also dependent on the number of antecedents of the fuzzy rules. As a result, robust aggregation strategies in OVO such as the weighted voting obtain poor results with this fuzzy classifier. In order to solve these problems, we propose to adapt the inference system of FARC-HD replacing the product t-norm with overlap functions. To do so, we define n-dimensional overlap functions. The usage of these new functions allows one to obtain more adequate outputs from the base classifiers for the subsequent aggregation in OVO and OVA schemes. Furthermore, we propose a new aggregation strategy for OVO to deal with the problem of the weighted voting derived from the inappropriate confidences provided by FARC-HD for this aggregation method. The quality of our new approach is analyzed using twenty datasets and the conclusions are supported by a proper statistical analysis. In order to check the usefulness of our proposal, we carry out a comparison against some of the state-of-the-art fuzzy classifiers. Experimental results show the competitiveness of our method.This work was supported in part by the Spanish Ministry of Science and
Technology under projects TIN2011-28488, TIN-2012-33856 and TIN-2013-
40765-P and the Andalusian Research Plan P10-TIC-6858 and P11-TIC-7765
Retrospective registration of tomographic brain images
In modern clinical practice, the clinician can make use of a vast array of specialized imaging techniques supporting diagnosis and treatment. For various reasons, the same anatomy of one patient is sometimes imaged more than once, either using the
same imaging apparatus (monomodal acquisition ), or different ones (multimodal acquisition). To make simultaneous use of the
acquired images, it is often necessary to bring these images in registration, i.e., to align their anatomical coordinate systems.
The problem of medical image registration as concerns human brain images is addressed in this thesis. The specific chapters
include a survey of recent literature, CT/MR registration using mathematical image features (edges and ridges), monomodal
SPECT registration, and CT/MR/SPECT/PET registration using image features extracted by the use of mathematical
morphology
Philosophical foundations of neuroeconomics: economics and the revolutionary challenge from neuroscience.
This PhD thesis focuses on the philosophical foundations of Neuroeconomics, an
innovative research program which combines findings and modelling tools from
economics, psychology and neuroscience to account for human choice behaviour. The
proponents of Neuroeconomics often manifest the ambition to foster radical
modifications in the accounts of choice behaviour developed by its parent disciplines.
This enquiry provides a philosophically informed appraisal of the potential for success
and the relevance of neuroeconomic research for economics. My central claim is that
neuroeconomists can help other economists to build more predictive and explanatory
models, yet are unlikely to foster revolutionary modifications in the economic theory of
choice.
The contents are organized as follows. In chapters 1-2, I present neuroeconomists’
investigative tools, distinguish the most influential approaches to neuroeconomic
research and reconstruct the case in favour of a neural enrichment of economic theory.
In chapters 3-7, I combine insights from neuro-psychology, economic methodology and
philosophy of science to develop a systematic critique of Neuroeconomics. In particular,
I articulate four lines of argument to demonstrate that economists are provisionally
justified in retaining a methodologically distinctive approach to the modelling of
decision making.
My first argument points to several evidential and epistemological concerns which
complicate the interpretation of neural data and cast doubt on the inferences
neuroeconomists often make in their studies. My second argument aims to show that the
trade-offs between the modelling desiderata that neuroeconomists and other economists
respectively value severely constrain the incorporation of neural insights into economic
models. My third argument questions neuroeconomists’ attempts to develop a unified
theory of choice behaviour by identifying some central issues on which they hold
contrasting positions. My fourth argument differentiates various senses of the term
‘revolution’ and illustrates that neuroeconomists are unlikely to provide revolutionary
contributions to economic theory in any of these senses