icts 2009 C05 Hansen
A NEW APPROACH FOR NEURAL EXPERT SYSTEMS
1
Gunawan 1, Hansen Virgantara 2
Program Pascasarjana Institut Teknologi Sepuluh Nopember
Kampus ITS Keputih, Sukolilo Surabaya
2
Sekolah Tinggi Teknik Surabaya
Ngagel Jaya Tengah 73-77 Surabaya
[email protected] 1, [email protected] 2
ABSTRACT
Hybrid Intelligent Systems are systems that
combine several intelligent technologies in order to
create new systems that have strength from several
systems and cover the weakness from others.
Combination Neural Network and Expert System
result a Neural Expert System. By combining
Neural Network and Expert System, they created a
new system that has ability to learn and can give
explanation to user about conclusion that have been
made. There are several approaches that integrate
neural networks and symbolic rules. In this paper,
we try to create an approach that can classify well
and it has priority of the most necessary information
so that it is capable in dealing with incomplete data.
Keywords: Neural Expert System, Hybrid
Intelligent
Systems,
Connectionist
Expert System.
1
INTRODUCTION
There are several intelligent technologies,
such as: probabilistic reasoning, fuzzy logic, neural
networks, and evolutionary computation. Each
technology has its strength and weaknesses. We
noticed that in many real-world applications, we
would need not only to acquire knowledge from
various sources, but also to combine different
intelligent technologies.
Knowledge in a rule-based expert system is
represented by IF-THEN production rules collected
by observing human experts. This task, called
knowledge acquisition, is difficult and expensive. In
addition, once the rules are store in the knowledge
base, they cannot be modified by the expert system
itself. Expert system cannot learn from experience
or adapt to new environments.
Knowledge in neural networks is stored as
synaptic between neurons. This knowledge is
obtained during the learning phase when a training
set of data is presented to the network. The network
propagates the input data from layer to layer until
the output data is generated. If it is different from
the desire output, an error is calculated and
propagated backwards through the network. Unlike
expert systems, neural networks learn without
human intervention.
In expert systems, knowledge can be divided
into individual rules and user can see and
understand the piece of knowledge applied by the
system. In contrast, in neural networks, one cannot
select a single synaptic weight as a discrete piece of
knowledge. Here the knowledge is embedded in the
entire network. Any change of a synaptic weight
may lead to unpredictable results.
An expert system cannot learn, but can
explain how it arrives to a particular solution. A
neural network can learn, but acts as a black box.
Thus by combine the advantages of each technology
we can create a more powerful and effective expert
system. Learning, generalization, robustness and
parallel information processing make neural
networks a right component for building a new
breed of expert systems.
2
ARCHITECTURE OF NEURAL
EXPERT SYSTEMS
A Neural Expert System can extract IFTHEN rules from the neural network, which enable
it to justify and explain its conclusion. Neural
networks only can produces weights from training
as knowledge. But it cannot explain the solution to
user. Thus will be produced IF-THEN rules from
the calculation of the weights.
Figure 1 shows the basic structure of a
neural expert system. It is consists of five parts:
• Inference Engine:
The heart of a neural expert system is the
inference engine. This controls the information
flow in the system and initiates inference over
the neural knowledge base. A neural inference
engine also ensures approximate reasoning.
Figure 1. Neural Expert System’s Architecture
•
21
ISSN 2085-1944
Neural Knowledge Base:
The 5t h Int ernat ional Conf erence on Inf ormat ion & Communicat ion Technology and Syst ems
22
Training Data
Neural Knowledge Base
Rule Extraction
New Data
Rule: IF - THEN
Inference Engine
Explanation Facilities
User Interface
User
•
•
•
3
Neural expert systems use a trained neural
network in place of knowledge base. The
neural network is capable of generalization. In
other words, the new input data does not have
to precisely match the data that was used in
network training. This allows neural expert
system to deal with incomplete and noisy data.
This ability is called approximate reasoning.
Rule Extraction:
The rule extraction unit examines the neural
knowledge base and produces the rule
implicitly from the trained neural networks.
Explanation Facilities:
The explanation facilities explain to the user
how the neural expert system arrives at a
particular solution when working with the new
input data.
User Interface:
The user interface provides the communication
between the user and the neural expert system.
RECENT WORKS
There are two approaches that can be used to
draw a conclusion with neural expert system. Each
approach have their own strength and weakness.
3.1
First Approach: Negnevitsky
This approach introduced by Negnevitsky in
2002. this approach needs target object, and then
system will give questions corresponding to the
object. Number of questions that given to user
depend on the user answers that given before.
This is uncommon as a classification
method, because it needs a class target object
wanted to achieve, but the main objective of a
classification is to find the target object based on
characteristics that are known. However, by
knowing what the target object is, the system can
provide questions that related to the object, so not
all information are required. Only the most
important information are required to draw a
conclusion.
How does a neural expert system extract
rules that justify its reference? Neurons in the
network are connected by links, each neurons has a
numerical weight attached to it. The weights in a
trained neural network determine the strength or
importance of the associated neuron inputs. This
characteristic is used for extracting rules.
Then system will determine whether that
obtained information is sufficient to draw a
conclusion or not. This following heuristic can be
applied (Gallant, 1993). An inference can be made
if the known net weighted input to a neuron is
greater than the sum of absolute values of the
weights of the unknown inputs.
3.2
Second Approach: Ioannis
Second approach for neural expert system is
introduced by Ioannis Hatzilygeroudis and Jim
Prentzas in 2004. Inference engine in this approach
is based in backward chaining algorithm. Inference
engine uses the working memory, which contains
facts required from the user to the inference
process.
The hybrid inference engine implements the
way neurules co-operate to reach a conclusion. It is
based on the firing ratio, a measurement of the
firing intention of a neurule, which is similar to the
convergence ratio, introduced in (Ghalwash 1998).
However it is possible to deduce the output of a
neurule without knowing all of the condition values.
To achieve this, known-sum and remaining-sum
must be defined as follows:
kn − sum = sf 0 + ∑ sf i * C i
(1)
rem − sum = ∑ sf i
(2)
i∈E
i∈U
i
Where E is the set of evaluated conditions, U
the set of unevaluated conditions and C i is the value
of condition cond i . So, known-sum is the weighted
sum of the values of the already known or evaluated
conditions (inputs) of the corresponding neurule
and remaining sum represents the largest possible
weighted sum of the remaining or unevaluated
conditions of the neurule. If |kn-sum| > rem-sum for
a certain neurule, then evaluation of its conditions
can stop, because its output can be deduced
regardless of the values of the unevaluated
conditions. In this case, its output is guaranteed to
be '-1' if kn-sum < 0 whereas it is ‘1’, if kn-sum > 0.
ISSN 2085-1944
C05- Integrating Neural Networks and Expert System into Neural Expert Systems – Gunawan
So, we define the firing ratio (fr) of a neurule as
follows:
fr =
kn − sum
rem − sum
(3)
The firing ratio of a neurule is an estimate of
its intention that its output will become ‘±1’.
Whenever fr > 1, the values of the evaluated
conditions can determine the value of its output,
regardless of the values of the unevaluated
conditions. The rule then evaluates to ‘1’ (true), if
kn-sum > 0 or to ‘-1’ (false), if kn-sum < 0.
4.
4
A NEW APPROACH TO NEURAL
EXPERT SYSTEM
On the third section, the approaches of
neural expert system are described with their own
strength and weaknesses. Now a new approach is
introduced to cover the weakness from those
approaches. Negnevitsky is good in expert system,
but uncommon as a classification method. Ioannis is
good in classification, but it cannot determine the
priority of information that mostly needed. By
combining the benefits from those approaches, it is
obtained a new system that can classify well and it
has priority of the most necessary information so
that it can dealing with incomplete data.
4.1
Algorithm
Algorithm for this approach (hybrid) is
similar with the Negnevitsky algorithm. But the
main difference is the best attribute always
recalculate in each iteration. So the priority of
information that needs by the system can be
different based on user input. More formally, the
inference algorithm is as follows:
1. Initialize array with the weights from each
neuron for sorting process.
2. Eliminate attributes that do not contributing.
An input is considered to be not contributing if
it does not move the net weighted input in the
opposite direction (combination weights must
be positives and negatives).
3. While the system has not reached a final
conclusion and there is unknown information
do:
3.1. Search the most important attribute from
the weights and the object is valid. The
most important attribute is determined by
maximum absolute value from the
weights.
3.2. Read input from user based on the most
important information.
4.2
23
3.3. Update values for the kn-sum and remsum for all target objects. If |known value|
> unknown value then stop with success.
kn-sum=kn-sum+weight QuestionIndex
rem-sum=rem-sum-weight QuestionIndex
3.4. Eliminate objects that do not appropriate
with the user input or move the net
weighted input in the opposite direction
(negative value). If an object is eliminated
then the object is set into invalid. Thus it
will not be used for determining the most
important attribute for the next iteration.
Determine conclusion if success, and the target
is an object with the biggest kn-sum.
Otherwise if not success then stop (failure) and
determine object as unknown.
Case Study
We use an example of Contact Lens dataset
to illustrate the functionalities of this approach. It is
a small dataset that consists of 24 instances and four
input attributes. There are three values of patient
age (young, pre-presbyopic, presbyopic), two
spectacles values (myope, hypermetrope), two
astigmatics values (no, yes), two tear production
rates values (reduced, normal), and three target
class of recommended lenses (none, soft, hard).
Content of contact lens dataset is shown in Table 2.
After pre-processing dataset and training
process, knowledge base for neural knowledge base
is as follows:
Table 1. Neural Knowledge Base
Contact Lens
None
None
None
None
None
None
Soft
Soft
Soft
Soft
Soft
Soft
Hard
Hard
Hard
Hard
Hard
Hard
ISSN 2085-1944
Attribute
Age: Young
Age: Pre-Presbyopic
Age: Presbyopic
Spectacle
Astigmatic
Tear Production Rate
Age: Young
Age: Pre-Presbyopic
Age: Presbyopic
Spectacle
Astigmatic
Tear Production Rate
Age: Young
Age: Pre-Presbyopic
Age: Presbyopic
Spectacle
Astigmatic
Tear Production Rate
Weight
-11.81
-6.32
-6.03
1.04
0.83
-11.86
25.76
25.76
17.44
7.84
-16.83
16.83
32.95
25.13
23.13
-8.25
16.16
16.16
The 5t h Int ernat ional
24
Conf erence on Inf ormat ion & Communicat ion Technology and Syst ems
Table 2. Contact Lens Dataset
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Age of The
Patient
young
young
young
young
young
young
young
young
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
Spectackle
myope
myope
myope
myope
hypermetrope
hypermetrope
hypermetrope
hypermetrope
myope
myope
myope
myope
hypermetrope
hypermetrope
hypermetrope
hypermetrope
myope
myope
myope
myope
hypermetrope
hypermetrope
hypermetrope
hypermetrope
Tear
Production
Recommended
Astigmatic
Rate
Lens
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
hard
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
hard
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
hard
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
none
no
reduced
none
no
normal
none
yes
reduced
none
yes
normal
hard
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
none
kn-sum>rem-sum check next iteration
After initialize all the weights, then we must
eliminate attribute age that do not contributing
(algorithm step 2), because all weights in each
object are positives and negatives. And finally there
are only three input attributes that can be used
(spectacle, astigmatic, and tear production rate).
Now start calculation for the first iteration.
In the first iteration, the most important
attribute is tear production rate (16.83). Then
system will ask user to answer what the input for
attribute tear production rate. If user answers
reduced (-1) then the calculation for kn-sum and
rem-sum as follows:
From the rules above, we can check the
accuracy that all inputs attribute tear production rate
is reduced (12 instances) then Recommended Lens
is none (also 12 instances). This make the accuracy
is 100%. Now let’s try another input if tear
production rate is normal (+1). The calculation for
kn-sum and rem-sum as follows:
None:
kn-sum = -11.86 * (-1)
= 11.86
rem-sum = |0.04| + |0.83| = 0.87
kn-sum>rem-sum success
None:
kn-sum = -11.86 * (+1) = -11.86
rem-sum = |0.04| + |0.83| = 0.87
kn-sum>rem-sum check next iteration
Soft:
kn-sum = 16.83 * (-1)
= -16.83
rem-sum = |7.84| + |-16.83|= 24.67
kn-sumrem-sum check next iteration
Soft:
kn-sum = 16.83 + (-16.83)*(-1) = 33.66
rem-sum = |7.84|
= 7.84
kn-sumrem-sum check next iteration
Target object Soft is success, so system will
conclude that:
IF Tear production Rate IS Reduced
AND Astigmatic IS No
THEN Recommended Lens is Soft
From the rules above, we can check the
accuracy that all inputs attribute tear production rate
is reduced and astigmatic is no (6 instances) then
Recommended Lens is none (5 instances, 1 instance
is recommended lens none). This make the accuracy
is 5/6*100%. = 83.33%.
If user input is unknown value, then kn-sum
is the average value of the significant factors of all
the homonymous condition and rem-sum will not
change. For example if input attribute age of patient
for target object none is unknown, then kn-sum will
be increased as much as the average of all age
weights (-11.81 -6.32 -6.03) / 3 = -8.05, but the
remaining sum will not decrease, because input
attribute age is still unevaluated or unknown.
If user input unknown value, it is no need to
check the success state, because the kn-sum never
more than the rem-sum. And this can be used to
increase performance of the system. For the next
iteration, the unknown attribute must be flagged so
it does not use for the most important attribute
again.
From examples above, we can see that the
accuracy of this approach is quite high depends on
input that given to the system and can deal with
noise and incomplete data.
5
25
CONCLUSION
In this paper, we presented a new approach
to neural expert system. Combination of Neural
Network and Expert System will create a more
powerful and effective expert system. It combines
the advantages of all systems and covers other
weaknesses. It is obtained a new system that has
ability to learn and can give explanation to the user.
The accuracy of the created rules during the
testing process is strongly influenced by the users
input. The questions that give to user is influenced
by weights obtained from the training process.
The first approach of neural expert system
(Negnevitsky) is good in expert system and can
determine the priority of information that mostly
needed. But it is uncommon as a classification
method because it needs target object wanted to
achieve.
The second approach of neural expert system
(Ioannis) is good in classification and can dealing
with incomplete data. But the accuracy of this
approach is highly influenced by the combination of
user inputs, and cannot determine what the most
important information. It is better to know what the
most important information so can dealing with
incomplete data.
The new approach combines the benefits of
both previous approaches. Thus it is obtained a new
system that can classify well and it has priority of
the most necessary information so that it is capable
in dealing with incomplete data
REFERENCES
[1] Hatzilygeroudis, I., & Prentzas, J. (2001)
HYMES: A Hybrid Modular Expert system
with Efficient Inference and Explanation:
Proceedings of the 8th Panhellenic
Conference on Informatics, Nicosia,
Cyprus, November, 2001 Vol.1 422-431.
[2] Hatzilygeroudis, I., & Prentzas, J. (2004)
Integrating (Rules Neural Networks) and
Cases for Knowledge Representation and
Reasoning in Expert Systems.
[3] Kuswara (2005) Paradigma Sistem Cerdas.
[4] Nard B. A., Neural Expert Systems: Survey
Paper for Cheng-568.
[5] Negnevitsky, Pearson Education. Hybrid
Intelligent System.
[6] Negnevistsky, Pearson Education. Rule
Base Expert System.
[7] Piyush Kedia, Ashish Arya, Nekhil
Agarwal, Alakh Gargiya. Expert Systems.
ISSN 2085-1944
26
The 5t h Int ernat ional Conf erence on Inf ormat ion & Communicat ion Technology and Syst ems
[8] Venkat Venkatasubramanian, Sourabh
Dash, Mano Ram Maurya, Priyan Patkar
ISSN 2085-1944
and Chunhua Zhao. Expert Systems –
Principles and Applications.
1
Gunawan 1, Hansen Virgantara 2
Program Pascasarjana Institut Teknologi Sepuluh Nopember
Kampus ITS Keputih, Sukolilo Surabaya
2
Sekolah Tinggi Teknik Surabaya
Ngagel Jaya Tengah 73-77 Surabaya
[email protected] 1, [email protected] 2
ABSTRACT
Hybrid Intelligent Systems are systems that
combine several intelligent technologies in order to
create new systems that have strength from several
systems and cover the weakness from others.
Combination Neural Network and Expert System
result a Neural Expert System. By combining
Neural Network and Expert System, they created a
new system that has ability to learn and can give
explanation to user about conclusion that have been
made. There are several approaches that integrate
neural networks and symbolic rules. In this paper,
we try to create an approach that can classify well
and it has priority of the most necessary information
so that it is capable in dealing with incomplete data.
Keywords: Neural Expert System, Hybrid
Intelligent
Systems,
Connectionist
Expert System.
1
INTRODUCTION
There are several intelligent technologies,
such as: probabilistic reasoning, fuzzy logic, neural
networks, and evolutionary computation. Each
technology has its strength and weaknesses. We
noticed that in many real-world applications, we
would need not only to acquire knowledge from
various sources, but also to combine different
intelligent technologies.
Knowledge in a rule-based expert system is
represented by IF-THEN production rules collected
by observing human experts. This task, called
knowledge acquisition, is difficult and expensive. In
addition, once the rules are store in the knowledge
base, they cannot be modified by the expert system
itself. Expert system cannot learn from experience
or adapt to new environments.
Knowledge in neural networks is stored as
synaptic between neurons. This knowledge is
obtained during the learning phase when a training
set of data is presented to the network. The network
propagates the input data from layer to layer until
the output data is generated. If it is different from
the desire output, an error is calculated and
propagated backwards through the network. Unlike
expert systems, neural networks learn without
human intervention.
In expert systems, knowledge can be divided
into individual rules and user can see and
understand the piece of knowledge applied by the
system. In contrast, in neural networks, one cannot
select a single synaptic weight as a discrete piece of
knowledge. Here the knowledge is embedded in the
entire network. Any change of a synaptic weight
may lead to unpredictable results.
An expert system cannot learn, but can
explain how it arrives to a particular solution. A
neural network can learn, but acts as a black box.
Thus by combine the advantages of each technology
we can create a more powerful and effective expert
system. Learning, generalization, robustness and
parallel information processing make neural
networks a right component for building a new
breed of expert systems.
2
ARCHITECTURE OF NEURAL
EXPERT SYSTEMS
A Neural Expert System can extract IFTHEN rules from the neural network, which enable
it to justify and explain its conclusion. Neural
networks only can produces weights from training
as knowledge. But it cannot explain the solution to
user. Thus will be produced IF-THEN rules from
the calculation of the weights.
Figure 1 shows the basic structure of a
neural expert system. It is consists of five parts:
• Inference Engine:
The heart of a neural expert system is the
inference engine. This controls the information
flow in the system and initiates inference over
the neural knowledge base. A neural inference
engine also ensures approximate reasoning.
Figure 1. Neural Expert System’s Architecture
•
21
ISSN 2085-1944
Neural Knowledge Base:
The 5t h Int ernat ional Conf erence on Inf ormat ion & Communicat ion Technology and Syst ems
22
Training Data
Neural Knowledge Base
Rule Extraction
New Data
Rule: IF - THEN
Inference Engine
Explanation Facilities
User Interface
User
•
•
•
3
Neural expert systems use a trained neural
network in place of knowledge base. The
neural network is capable of generalization. In
other words, the new input data does not have
to precisely match the data that was used in
network training. This allows neural expert
system to deal with incomplete and noisy data.
This ability is called approximate reasoning.
Rule Extraction:
The rule extraction unit examines the neural
knowledge base and produces the rule
implicitly from the trained neural networks.
Explanation Facilities:
The explanation facilities explain to the user
how the neural expert system arrives at a
particular solution when working with the new
input data.
User Interface:
The user interface provides the communication
between the user and the neural expert system.
RECENT WORKS
There are two approaches that can be used to
draw a conclusion with neural expert system. Each
approach have their own strength and weakness.
3.1
First Approach: Negnevitsky
This approach introduced by Negnevitsky in
2002. this approach needs target object, and then
system will give questions corresponding to the
object. Number of questions that given to user
depend on the user answers that given before.
This is uncommon as a classification
method, because it needs a class target object
wanted to achieve, but the main objective of a
classification is to find the target object based on
characteristics that are known. However, by
knowing what the target object is, the system can
provide questions that related to the object, so not
all information are required. Only the most
important information are required to draw a
conclusion.
How does a neural expert system extract
rules that justify its reference? Neurons in the
network are connected by links, each neurons has a
numerical weight attached to it. The weights in a
trained neural network determine the strength or
importance of the associated neuron inputs. This
characteristic is used for extracting rules.
Then system will determine whether that
obtained information is sufficient to draw a
conclusion or not. This following heuristic can be
applied (Gallant, 1993). An inference can be made
if the known net weighted input to a neuron is
greater than the sum of absolute values of the
weights of the unknown inputs.
3.2
Second Approach: Ioannis
Second approach for neural expert system is
introduced by Ioannis Hatzilygeroudis and Jim
Prentzas in 2004. Inference engine in this approach
is based in backward chaining algorithm. Inference
engine uses the working memory, which contains
facts required from the user to the inference
process.
The hybrid inference engine implements the
way neurules co-operate to reach a conclusion. It is
based on the firing ratio, a measurement of the
firing intention of a neurule, which is similar to the
convergence ratio, introduced in (Ghalwash 1998).
However it is possible to deduce the output of a
neurule without knowing all of the condition values.
To achieve this, known-sum and remaining-sum
must be defined as follows:
kn − sum = sf 0 + ∑ sf i * C i
(1)
rem − sum = ∑ sf i
(2)
i∈E
i∈U
i
Where E is the set of evaluated conditions, U
the set of unevaluated conditions and C i is the value
of condition cond i . So, known-sum is the weighted
sum of the values of the already known or evaluated
conditions (inputs) of the corresponding neurule
and remaining sum represents the largest possible
weighted sum of the remaining or unevaluated
conditions of the neurule. If |kn-sum| > rem-sum for
a certain neurule, then evaluation of its conditions
can stop, because its output can be deduced
regardless of the values of the unevaluated
conditions. In this case, its output is guaranteed to
be '-1' if kn-sum < 0 whereas it is ‘1’, if kn-sum > 0.
ISSN 2085-1944
C05- Integrating Neural Networks and Expert System into Neural Expert Systems – Gunawan
So, we define the firing ratio (fr) of a neurule as
follows:
fr =
kn − sum
rem − sum
(3)
The firing ratio of a neurule is an estimate of
its intention that its output will become ‘±1’.
Whenever fr > 1, the values of the evaluated
conditions can determine the value of its output,
regardless of the values of the unevaluated
conditions. The rule then evaluates to ‘1’ (true), if
kn-sum > 0 or to ‘-1’ (false), if kn-sum < 0.
4.
4
A NEW APPROACH TO NEURAL
EXPERT SYSTEM
On the third section, the approaches of
neural expert system are described with their own
strength and weaknesses. Now a new approach is
introduced to cover the weakness from those
approaches. Negnevitsky is good in expert system,
but uncommon as a classification method. Ioannis is
good in classification, but it cannot determine the
priority of information that mostly needed. By
combining the benefits from those approaches, it is
obtained a new system that can classify well and it
has priority of the most necessary information so
that it can dealing with incomplete data.
4.1
Algorithm
Algorithm for this approach (hybrid) is
similar with the Negnevitsky algorithm. But the
main difference is the best attribute always
recalculate in each iteration. So the priority of
information that needs by the system can be
different based on user input. More formally, the
inference algorithm is as follows:
1. Initialize array with the weights from each
neuron for sorting process.
2. Eliminate attributes that do not contributing.
An input is considered to be not contributing if
it does not move the net weighted input in the
opposite direction (combination weights must
be positives and negatives).
3. While the system has not reached a final
conclusion and there is unknown information
do:
3.1. Search the most important attribute from
the weights and the object is valid. The
most important attribute is determined by
maximum absolute value from the
weights.
3.2. Read input from user based on the most
important information.
4.2
23
3.3. Update values for the kn-sum and remsum for all target objects. If |known value|
> unknown value then stop with success.
kn-sum=kn-sum+weight QuestionIndex
rem-sum=rem-sum-weight QuestionIndex
3.4. Eliminate objects that do not appropriate
with the user input or move the net
weighted input in the opposite direction
(negative value). If an object is eliminated
then the object is set into invalid. Thus it
will not be used for determining the most
important attribute for the next iteration.
Determine conclusion if success, and the target
is an object with the biggest kn-sum.
Otherwise if not success then stop (failure) and
determine object as unknown.
Case Study
We use an example of Contact Lens dataset
to illustrate the functionalities of this approach. It is
a small dataset that consists of 24 instances and four
input attributes. There are three values of patient
age (young, pre-presbyopic, presbyopic), two
spectacles values (myope, hypermetrope), two
astigmatics values (no, yes), two tear production
rates values (reduced, normal), and three target
class of recommended lenses (none, soft, hard).
Content of contact lens dataset is shown in Table 2.
After pre-processing dataset and training
process, knowledge base for neural knowledge base
is as follows:
Table 1. Neural Knowledge Base
Contact Lens
None
None
None
None
None
None
Soft
Soft
Soft
Soft
Soft
Soft
Hard
Hard
Hard
Hard
Hard
Hard
ISSN 2085-1944
Attribute
Age: Young
Age: Pre-Presbyopic
Age: Presbyopic
Spectacle
Astigmatic
Tear Production Rate
Age: Young
Age: Pre-Presbyopic
Age: Presbyopic
Spectacle
Astigmatic
Tear Production Rate
Age: Young
Age: Pre-Presbyopic
Age: Presbyopic
Spectacle
Astigmatic
Tear Production Rate
Weight
-11.81
-6.32
-6.03
1.04
0.83
-11.86
25.76
25.76
17.44
7.84
-16.83
16.83
32.95
25.13
23.13
-8.25
16.16
16.16
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Conf erence on Inf ormat ion & Communicat ion Technology and Syst ems
Table 2. Contact Lens Dataset
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Age of The
Patient
young
young
young
young
young
young
young
young
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
pre-presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
presbyopic
Spectackle
myope
myope
myope
myope
hypermetrope
hypermetrope
hypermetrope
hypermetrope
myope
myope
myope
myope
hypermetrope
hypermetrope
hypermetrope
hypermetrope
myope
myope
myope
myope
hypermetrope
hypermetrope
hypermetrope
hypermetrope
Tear
Production
Recommended
Astigmatic
Rate
Lens
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
hard
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
hard
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
hard
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
none
no
reduced
none
no
normal
none
yes
reduced
none
yes
normal
hard
no
reduced
none
no
normal
soft
yes
reduced
none
yes
normal
none
kn-sum>rem-sum check next iteration
After initialize all the weights, then we must
eliminate attribute age that do not contributing
(algorithm step 2), because all weights in each
object are positives and negatives. And finally there
are only three input attributes that can be used
(spectacle, astigmatic, and tear production rate).
Now start calculation for the first iteration.
In the first iteration, the most important
attribute is tear production rate (16.83). Then
system will ask user to answer what the input for
attribute tear production rate. If user answers
reduced (-1) then the calculation for kn-sum and
rem-sum as follows:
From the rules above, we can check the
accuracy that all inputs attribute tear production rate
is reduced (12 instances) then Recommended Lens
is none (also 12 instances). This make the accuracy
is 100%. Now let’s try another input if tear
production rate is normal (+1). The calculation for
kn-sum and rem-sum as follows:
None:
kn-sum = -11.86 * (-1)
= 11.86
rem-sum = |0.04| + |0.83| = 0.87
kn-sum>rem-sum success
None:
kn-sum = -11.86 * (+1) = -11.86
rem-sum = |0.04| + |0.83| = 0.87
kn-sum>rem-sum check next iteration
Soft:
kn-sum = 16.83 * (-1)
= -16.83
rem-sum = |7.84| + |-16.83|= 24.67
kn-sumrem-sum check next iteration
Soft:
kn-sum = 16.83 + (-16.83)*(-1) = 33.66
rem-sum = |7.84|
= 7.84
kn-sumrem-sum check next iteration
Target object Soft is success, so system will
conclude that:
IF Tear production Rate IS Reduced
AND Astigmatic IS No
THEN Recommended Lens is Soft
From the rules above, we can check the
accuracy that all inputs attribute tear production rate
is reduced and astigmatic is no (6 instances) then
Recommended Lens is none (5 instances, 1 instance
is recommended lens none). This make the accuracy
is 5/6*100%. = 83.33%.
If user input is unknown value, then kn-sum
is the average value of the significant factors of all
the homonymous condition and rem-sum will not
change. For example if input attribute age of patient
for target object none is unknown, then kn-sum will
be increased as much as the average of all age
weights (-11.81 -6.32 -6.03) / 3 = -8.05, but the
remaining sum will not decrease, because input
attribute age is still unevaluated or unknown.
If user input unknown value, it is no need to
check the success state, because the kn-sum never
more than the rem-sum. And this can be used to
increase performance of the system. For the next
iteration, the unknown attribute must be flagged so
it does not use for the most important attribute
again.
From examples above, we can see that the
accuracy of this approach is quite high depends on
input that given to the system and can deal with
noise and incomplete data.
5
25
CONCLUSION
In this paper, we presented a new approach
to neural expert system. Combination of Neural
Network and Expert System will create a more
powerful and effective expert system. It combines
the advantages of all systems and covers other
weaknesses. It is obtained a new system that has
ability to learn and can give explanation to the user.
The accuracy of the created rules during the
testing process is strongly influenced by the users
input. The questions that give to user is influenced
by weights obtained from the training process.
The first approach of neural expert system
(Negnevitsky) is good in expert system and can
determine the priority of information that mostly
needed. But it is uncommon as a classification
method because it needs target object wanted to
achieve.
The second approach of neural expert system
(Ioannis) is good in classification and can dealing
with incomplete data. But the accuracy of this
approach is highly influenced by the combination of
user inputs, and cannot determine what the most
important information. It is better to know what the
most important information so can dealing with
incomplete data.
The new approach combines the benefits of
both previous approaches. Thus it is obtained a new
system that can classify well and it has priority of
the most necessary information so that it is capable
in dealing with incomplete data
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