Biomedical Engineering Reference
In-Depth Information
cognition and linguistic language (Trillas and
Guadarrama, 2005).
The issue of interpretability is particularly
relevant in classification based problems, but
overlooked, since so often the concomitant analy-
sis is more oriented to the resultant classification
accuracy, rather than interpretability. Indeed,
Breiman (2001), in an informative discussion on
the cultures of statistical modelling, comes down
heavily on the need to accommodate the ability
to interpret results in analysis undertaken. Their
discussion offers a pertinent illustrative argument,
describing the example of a medical doctor, with
experimental data, in a choice between accuracy
and interpretability they would choose interpret-
ability. This issue of interpretability over accuracy
is also pertinent in general biology.
This chapter considers fuzzy decision trees
(FDTs), an example of an already established
technique that has been developed using FST.
The fundamentals of the decision tree technique,
within a crisp or fuzzy environment, is concerned
with the classification of objects described by a
number of attributes, with concomitant decision
rules derived in the constructed decision tree. The
inherent structure a consequence of the partition-
ing algorithm used to discern the classification
impact of the attributes. An early FDT reference
is attributed to Chang and Pavlidis (1977). In the
area of medicine, for example, Podgorelec
et al
.
(2002) offer a good review of decision trees, with
a most recent employment of FDTs presented
in Armand
et al
. (2007), which looked into gait
deviations. Relative to this, there is a comparative
dearth of their employment in a biological setting,
one exception being Beynon
et al
. (2004a).
An important feature of FDTs is the con-
comitant sets of fuzzy '
if
..
then
..' decision rules
constructed, whose condition and decision parts,
using concomitant attributes, can be described in
linguistics terms (such as low, medium or high).
The suggested FDT approach employed here was
presented in Yuan and Shaw (1995) and Wang
et
al
. (2000), and attempts to include the cognitive
uncertainties evident in the data values. This FDT
approach has been used in Beynon
et al
. (2004b)
and Beynon
et al
. (2004a), the latter investigating
the songflight of the Sedge Warbler, expositing
the relationship between the birds' characteristics
like, repertoire size and territory size, against
their song flight duration.
Central to the utilisation of FDTs is the fuzzi-
fication of the considered data set, through the
employment of FST related membership functions
(MFs), which further enable the linguistic repre-
sentation of the attributes considered (Kecman,
2001), present also in the subsequent decision rules
constructed. Alongside the exposition of FDTs
in this chapter, the results from two contrasting
biology based applications are considered; the
first using a well known data set from the UCI
data repository and relates to the prediction of
the age of abalones (Waugh, 1995), the second is
a more contemporary application looking at the
torpor bouts of hibernating Greater Horseshoe
bats (Park
et al
., 2000).
The contribution of this topic chapter is the
clear understanding of the potential advantages
of the utilization of FDTs in biological processes.
The small hypothetical example allows the reader
to clearly follow the included analytical rudi-
ments, and the larger applications demonstrate
the interpretability allowed through the use of
this approach. It expected a reader will gain ef-
fective insights into the type of technical details
necessary for a FDT analysis, and its analysis
within biological problems.
bACkGROUND
The contents of this section are partitioned into
three subsections; the first introduces fuzzy set
theory including the fuzzification of a small
example data set, the second outlines the FDT
technique used to exposit the concomitant general
Search WWH ::
Custom Search