![](bg2.png)
improve the quality of measurement devices, using
the opportunity that we had to create the whole Fleet
Management System, we decided to go one step
further and focus on the analysis of the final data in
order to decide whether there had been refueling,
fuel theft or maybe the fuel level change was caused
by the regular usage of the vehicle.
Intelligent decision support systems are able to
classify an event based on the feature vector with a
very good accuracy. Unfortunately, using those
systems in the industry is still minor, especially in
the areas where Quality of Service has crucial role,
due to concerns that an unexpected error may be
committed. Wrong decisions in the case of real
systems are mainly due to the lack of appropriate
datasets. Very often, we have to deal with
imbalanced datasets, where the size of dataset that
represents one class of the problem is much bigger
than the other classes. The problem of analysis of
the fuel level in the tank is also such example. It is
very easy to get information about changes in the
level of fuel in typical operating conditions of the
vehicle, but it is difficult to gather learning data that
represent different ways of fuel theft or extreme
operating conditions. In this paper, in order to
increase the accuracy of the decision, two techniques
have been used. The first one is very well known
Multiple Classifiers System idea (MCS) (Dietterich,
2000), (Kuncheva, 2004) that makes decisions based
on the fusion of the outputs from all of the classifiers
in the ensemble. MCS are very strongly developed,
mostly because of the fact that committee, also
known as an ensemble, can outperform its members
(Kittler, et al., 2006). Due to the fact that we are
dealing with imbalanced datasets, diversity measure
was also used. Note that even the best MCS will not
be able to outperform its members if classifiers in
the team are identical. A very important issue is to
increase the diversity between the members in case
of wrong output, while maintaining high accuracy of
individual classifiers in the pool. Furthermore,
diversity measure doesn't bring any benefits if all of
the members in the ensemble have a very good
accuracy.
In this paper, it is shown that the MCS built with
Random Reference Classifier (RRC) (Woloszynski et
al., 2010) used in the analysis of the fuel level
changes can provide very good results. In the first
part of Section 5, it is shown that the RRC behaves
as expected for imbalanced and balanced datasets
(Wang et al., 2013). For this purpose artificially
created datasets were used. The next step was to
verify if the created MCS, constructed with RRC is
able to identify the fuel level changes correctly
based on the actual data. The different types of
diversity measures have been used, both the pairwise
and nonpairwise measures (Kuncheva, 2004) and the
influence on the MCS performance was shown.
The paper is organized as follows. In the section
2 methods of measuring fuel with their advantages
and disadvantages are discussed. In the section 3, the
whole architecture of the IT system that has been
created in order to analyze the data was described. In
the section 4 the exact problem description is
presented. The following sections discuss the
experiments that have been carried out, we present
the results and conclusions that may be noticed. We
also present opportunities for the further research.
1.1 Motivation
It may not be clear why such comprehensive
technique as MCS was used to answer relatively
simple question. It has to be noted that identifying
the typical fuel increase, especially when the fuel
amount change is big, is not a problem. The most
difficult task is to detect a small refueling or
advanced method of fuel stealing. Small refueling
may occur when the vehicle cannot reach the home
station where the cost of the fuel is relatively low. In
such circumstances it may be necessary to detect i.e.
40 liters change in 800 liter tank.
Detecting the fuel stealing is even more
complicated. There are multiple ways to steal the
fuel from the car. It has to be noted that the simplest
method, where filler flap is opened and closed, and
subsequently a fuel decrease is detected is rare. The
very common method is to drain fuel from the fuel
wire into external container, during the 8 hours stop.
In this case the MCS is more like the decision
support system. The system can analyse if the fuel
was stolen or it was used by external fuel device, i.e.
Webasto. This approach can improve the process of
detecting the changes of the fuel, which in most
cases is executed by the FMS system user.
2 FUEL LEVEL MEASUREMENT
METHODS
There are several methods to measure the fuel level
in the tank and its consumption by the vehicle.
Depending on the operating conditions and type of
the vehicle only some of them may be used. Below
the overview of the most popular methods with
information about their advantages and
disadvantages is presented.
TheImpactoftheDiversityonMultipleClassifierSystemPerformance-IdentifyingChangesintheAmountofFuelinthe
FleetManagementSystem
349