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Article

# A Rule-Based Method to Locate the Bounds of Neural Networks

Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece
*
Author to whom correspondence should be addressed.
Knowledge 2022, 2(3), 412-428; https://doi.org/10.3390/knowledge2030024
Submission received: 24 May 2022 / Revised: 28 July 2022 / Accepted: 9 August 2022 / Published: 11 August 2022

## Abstract

:
An advanced method of training artificial neural networks is presented here which aims to identify the optimal interval for the initialization and training of artificial neural networks. The location of the optimal interval is performed using rules evolving from a genetic algorithm. The method has two phases: in the first phase, an attempt is made to locate the optimal interval, and in the second phase, the artificial neural network is initialized and trained in this interval using a method of global optimization, such as a genetic algorithm. The method has been tested on a range of categorization and function learning data and the experimental results are extremely encouraging.

## 1. Introduction

Artificial neural networks (ANNs) are programming tools [1,2] based on a series of parameters that are commonly called weights or processing units. They have been used in a variety of problems from different scientific areas, such as physics [3,4,5], solving differential equations [6,7], agriculture [8,9], chemistry [10,11,12], economics [13,14,15], medicine [16,17], etc. A common way to express a neural network is a function $N ( x → , w → )$, with $x →$ the input vector (commonly called the pattern) and $w →$ the weight vector. A method that trains a neural network should be used to estimate the vector $w →$ for a certain problem. The training procedure can also be formulated as an optimization problem, where the target is to minimize the so-called error function:
$E N x → , w → = ∑ i = 1 M N x → i , w → − y i 2$
In Equation (1), the set $x i → , y i , i = 1 , … , M$, is the dataset used to train the neural network, with $y i$ being the actual output for the point $x i →$. The neural network $N ( x → , w → )$ can be modeled as a summation of processing units, as proposed in [18]:
$N x → , w → = ∑ i = 1 H w ( d + 2 ) i − ( d + 1 ) σ ∑ j = 1 d x j w ( d + 2 ) i − ( d + 1 ) + j + w ( d + 2 ) i$
with H the number of processing units in the neural network and d the dimension of vector $x →$. The function $σ ( x )$ is the sigmoid function defined as:
$σ ( x ) = 1 1 + exp ( − x )$
From Equation (2), one can obtain that the dimension of the weight vector w is computed as: $n = ( d + 2 ) H$. The function of Equation (1) has been minimized with a variety of optimization methods during the past years such as: the back propagation method [19,20], the RPROP method [21,22,23], quasi-Newton methods [24,25], simulated annealing [26,27], genetic algorithms [28,29], particle swarm optimization [30,31] etc. In addition, various researchers have worked on the initialization of the weights of neural networks, such as initialization using decision trees [32], an initialization method based on Cauchy’s inequality [33], a method based on discriminant learning [34], etc. Another topic that has attracted the interest of many researchers is weight decaying, which is a regularization method that adapts the weights of the network aiming to avoid the overfitting problem. Several papers have appeared in this area with methods such as those with positive correlation [35], the SarProp algorithm [36], the incorporation of pruning techniques [37], etc. In addition, more advanced and more recent techniques from the area of computational intelligence have been proposed for neural network training such as the differential evolution method [38,39], the construction of neural networks with ant colony optimization [40], the construction of neural networks using grammatical evolution to solve differential equations [41], etc. Furthermore, due to development of GPU units, a lot of works have been published that take advantage of these processing units [42,43].
The present work proposes an innovative interval generation technique for the initialization and training of artificial neural network parameters. This new method has its roots in interval methods [44,45,46]. In the current work, using arithmetic intervals, a set of rules for dividing the initial interval for the parameters of an artificial neural network is constructed. The construction is carried out using a hybrid genetic algorithm, in which chromosomes are the set of division rules. After the termination of the genetic algorithm, the artificial neural network is initialized in the interval resulting from the application of the optimal partitioning rules and then trained using a genetic algorithm.
The method used has two objectives: the first objective is to detect a small interval of initialization for the parameters of the artificial neural network and the second objective is to accelerate the training of the network. In the first target, using information from the training data, the algorithm will make an attempt to identify the interval that will ultimately give better results. In the second objective, once a small-value interval has been detected, a global optimization method can be used more efficiently to detect the lowest value of the network error.
The proposed method is expected to achieve significant results since in principle it has all the advantages of genetic algorithms, such as tolerance for errors, possibilities for parallel implementation, the efficient exploration of the research space, etc. In addition, the first phase of the method will reduce the volume of the possible values for the weights so that in the second phase the search for the global minimum of the network error function will become more efficient and faster.
The proposed methodology can even be applied to different types of artificial neural networks such as recurrent neural networks [47,48]. A simple recurrent neural network can be expressed as single neural cell with a single input, a single output and a state (also known as the memory of the cell). Given the input of the cell $x ( t )$ at step t and the previous state of the cell $h ( t − 1 )$ at step $t − 1$, the updated state of the cell $h ( t )$ is estimated as shown in the equation:
$h ( t ) = f W h h ∗ h ( t − 1 ) + W x h ∗ x ( t ) + b h$
$y ( t ) = σ W h y ∗ h ( t ) + b y$
where the $f ( x )$ function is usually the softmax function. The proposed method can be used here to estimate a promising bounding box for the vector parameters $W$ and $b$ of the network before any other training method is applied.
The rest of this article is as follows: in Section 2 the proposed method is discussed in detail, in Section 3 the experimental datasets as well as the results from the application of the proposed method are provided and finally in Section 4 some conclusions and guidelines for future enhancements are presented.

## 2. Method Description

The proposed method consists of two major steps: in the first step, the construction of partition rules for the initial value interval for the parameters of the artificial neural network is made, and in the second step, the artificial neural network is initialized in the optimal space resulting from the first step and training takes place. The training is performed through a second genetic algorithm. In the first genetic algorithm, the chromosomes are sets of partition rules for the initial value interval of the artificial neural network, and in the second genetic algorithm, the chromosomes are the parameters of the artificial neural network. It is obvious that this is a time-consuming process and modern parallel techniques such as the OpenMP [49] library must be used to accelerate it. The first genetic algorithm is analyzed in Section 2.1 and the second in Section 2.5.

#### 2.1. Locating the Best Rules

Firstly, we introduce the rule set $I n$ where:
$I n = l 1 , r 1 , l 2 , r 2 , … , l n , r n$
where $l i ∈ 0 , 1 , r i ∈ 0 , 1$ and $i = 1 , … , n$. The set $I n$ defines the set of partition rules for a function defined as
$f : S → R , S ⊂ R n$
with S:
$S = a 1 , b 1 ⊗ a 2 , b 2 ⊗ … a n , b n$
If $l i = 1$ then $a i = a i 2$ and if $r i = 1$ then $b i = b i 2$. For example, consider the Rastrigin function:
$f ( x ) = x 1 2 + x 2 2 − cos 18 x 1 − cos 18 x 2 , x ∈ [ − 1 , 1 ] 2$
Also consider the set $I 2 = 1 , 0 , 0 , 1$. The produced bounding box for the Rastrigin function is now $S ′ = − 0.5 , 1 × − 1 , 0.5$.
Subsequently, we introduce the extended set $C K n$ as a set of production rules defined as:
$R K n = I n ( 1 ) , I n ( 2 ) , … , I n ( K ) ,$
where $I n ( i ) , i = 1 , … , K$, are the rule sets of Equation (6). For example, let $K = 2$ for the Rastrigin function and $R 22 = 0 , 1 , 1 , 0 , 1 , 0 , 1 , 1$. The final bounding box is considered after applying the sets $0 , 1 , 1 , 0$ and $1 , 0 , 1 , 1$ in the original box S. The computation steps are:
• Apply$0 , 1 , 1 , 0$ to S, yielding $S ′ = − 0.5 , 1 × − 1 , 0.5$.
• Apply$1 , 0 , 1 , 1$ to $S ′$, yielding $S ″ = − 0.25 , 1 × − 0.5 , 0.25$.
We consider chromosomes in the form of Equation (10) for the first phase of the proposed method. The value n is the total number of parameters for the neural network. The fitness of every chromosome g is an interval $f g = f g , min , f g , max$. Hence, in order to compare two different intervals $a = a 1 , a 2$ and $b = b 1 , b 2$, we incorporate the following function:
$L * ( a , b ) = TRUE , a 1 < b 1 , OR a 1 = b 1 AND a 2 < b 2 FALSE , OTHERWISE$
Hence, the steps of the genetic algorithm of the first phase are the following:

#### 2.1.1. Initialization Step

• SetK as the number of rules.
• Set$S = − D , D n$ as the initial bounding box for the parameters of the neural network. D is considered as a positive number with $D > 1$.
• Set$N C$ as the total number of chromosomes.
• Set$N S$ as the number of samples in the fitness evaluation.
• Set$P s$ as the selection rate, where $P s ≤ 1$.
• Set$P m$ as the mutation rate, where $P m ≤ 1$.
• Set$t = 0$ as the current generation number.
• Set$N t$ as the maximum number of generations allowed.
• Initialize randomly the chromosomes $C i , i = 1 , … , N C$, as sets of Equation (10).

#### 2.1.2. Termination Check Step

• Set$t = t + 1$.
• If$t ≥ N t$, terminate.

#### 2.1.3. Genetic Operations Step

• For every chromosome $C i , i = 1 , … , N C$, calculate the corresponding fitness value $f i$ using the algorithm in Section 2.2.
• Apply the selection operator. Initially, the chromosomes are sorted according to their fitness values. The sorting utilizes the function $L * ( a , b )$ of Equation (11) to compare fitness values. The best $1 − P s × N c$ are copied to the next generation while the rest of them are substituted by offspring created through the crossover procedure. The mating parents for the crossover procedure are selected using the well-known technique of tournament selection.
• Apply the crossover operator: For every pair of selected parents $( z , w ) ,$ two children $( c z , c w )$ are produced using the uniform crossover procedure described in Section 2.3.
• Apply the mutation operator using the algorithm in Section 2.4.
• Goto Termination Check Step.

#### 2.2. Fitness Evaluation for the Rule Genetic Algorithm

The fitness value for each chromosome g is considered as an interval $f = f min , f max$, where $f min$ is an estimation of the lower value obtained using the rules of the chromosome g and $f max$ is an estimation of the maximum value. In order to calculate the fitness of every set of rules C, the following steps are performed:
• Set$f min = ∞$.
• Set$f max = − ∞$.
• Apply the rule set g to the original bounding box S. The outcome of this application is the new bounding box $S g$.
• For$i = 1 , … , N S$do
(a)
Produce a random sample $w ∈ S g$.
(b)
Calculate the training error $E g = E ( N ( x → , w → ) )$ using Equation (1).
(c)
If$E g ≤ f min$ then $f min = E g$.
(d)
If$E g ≥ f max$ then $f max = E g$.
• EndFor
• Return the interval $f = f min , f max$ as the fitness of chromosome $g .$

#### 2.3. Crossover for the Rule Genetic Algorithm

The crossover for the genetic algorithm of the first phase is performed using uniform crossover. For every couple $( z , w )$ of selected parents, two children $( c z , c w )$ are produced through the following procedure:
• For$i = 1 … K$do
(a)
Let$z ( i ) = l z ( i ) , r z ( i )$ be the i-th item of the chromosome z.
(b)
Let$w ( i ) = l w ( i ) , r w ( i )$ be the i-th item of the chromosome w.
(c)
Produce a random number $r ≤ 1$.
(d)
If$r ≤ 0.5$then
• Set$c z ( i ) = l z ( i ) , r w ( i )$.
• Set$c w ( i ) = l w ( i ) , r z ( i )$.
(e)
Else
• Set$c z ( i ) = l w ( i ) , r z ( i )$.
• Set$c w ( i ) = l z ( i ) , r w ( i )$.
(f)
Endif
• EndFor

#### 2.4. Mutation for the Rule Genetic Algorithm

The steps for the mutation procedure for the genetic algorithm of the first phase are the following:
• For$i = 1 , … , N C$do
(a)
Let$C i = C i ( 1 ) , C i ( 2 ) , … , C i ( K )$ be the i-th chromosome of the population.
(b)
For$j = 1 , … , K$do
• Let$C i ( j ) = l i ( j ) , r i ( j )$.
• Take$r ≤$1 a random number.
• If$r ≤ P m$then alter randomly with probability 50% the $l i ( j )$ or the $r i ( j )$ part of $C i ( j )$.
(c)
EndFor
• EndFor

#### 2.5. Second Phase

In the second phase, the best chromosome $g b$ defined as
$g b = l b , 1 , r b , 1 , l b , 2 , r b , 2 , … , l b , K , r b , K$
is used to transform the original bounding box $S = [ − F , F ] ( n )$ into a new box $S b$. The new hyperbox is defined as
$S b = a g , 1 , b g , 1 × a g , 2 , b g , 2 × … × a g , n , b g , n$
This hyperbox will be used to bound the parameters of the neural network. The parameters of the network are trained using a genetic algorithm with the following steps:

#### 2.5.1. Initialization Step

• Set$N C$ as the total number of chromosomes.
• Set$P s$ as the selection rate, where $P s ≤ 1$.
• Set$P m$ as the mutation rate, where $P m ≤ 1$.
• Set$t = 0$ as the current generation number.
• Set$N t$ as the maximum number of generations allowed.
• Initialize randomly the chromosomes $C i , i = 1 , … , N C$, inside the bounding box $S b$.

#### 2.5.2. Termination Check Step

• Set$t = t + 1$.
• If$t ≥ N t$goto Local Search Step.

#### 2.5.3. Genetic Operations Step

• Calculate the fitness value of every chromosome.
(a)
For$i = 1 … N C$Do
• Set$f i = E ( N ( x → , C i ) )$ using Equation (1).
(b)
EndFor
• Apply the crossover operator. In this phase, the best $1 − P s × N c$ chromosomes are transferred intact to the next generation. The rest of the chromosomes are substituted by offspring created through crossover. The selection of two parents $x = x 1 , x 2 , … , x n$ and $y = y 1 , y 2 , … , y n$ for crossover is performed using tournament selection. Having selected the parents, the offspring $x ˜$ and $y ˜$ are formed using the following:
$x i ˜ = r i x i + 1 − r i y i y i ˜ = r i y i + 1 − r i x i$
where $r i$ are random numbers in $[ − 0.5 , 1.5 ]$ [43].
• Apply the mutation operator. The mutation scheme is the same as in the work of Kaelo and Ali [50]:
(a)
For$i = 1 … N C$do
• For$j = 1 … n$do
• Let$r ∈ [ 0 , 1 ]$ be a random number.
• If$r ≤ P m$ alter the element $C i j$ using the following:
$C i j = C i j + Δ t , b g , i − C i j t = 0 C i j − Δ t , C i j − a g , i t = 1$
where t is a random number that takes either the value 0 or 1 and $Δ ( t , y )$ is calculated as:
$Δ ( t , y ) = y 1 − r 1 − t N t z$
where $r ∈ [ 0 , 1 ]$ is a random number and z is a user-defined parameter.
• EndFor
(b)
EndFor
• Goto Termination check step.

#### 2.5.4. Local Search Step

• Set$C *$ as the best chromosome of the population.
• Apply a local search procedure $C * = L C *$. The local search procedure used here is a BFGS method of Powell [51].

## 3. Experiments

The proposed method was evaluated on a series of classification and regression problems from the relevant literature. The classification problems used for the experiments were found in most cases in two internet databases:
The regression datasets were in most cases available from the Statlib URL http://lib.stat.cmu.edu/datasets/ (accessed on 23 May 2022). The proposed method was compared against a neural network trained by a genetic algorithm and the results are reported.

#### 3.1. Experimental Datasets

The following classification datasets were used:
• Appendicitis, a medical dataset, proposed in [53].
• Australian dataset [54], which is related to credit card applications.
• Balance dataset [55], which is used to predict psychological states.
• Cleveland dataset, a dataset used to detect heart disease used in various papers [56,57].
• Bands dataset, a printing problem used to identify cylinder bands.
• Dermatology dataset [58], which is used for the differential diagnosis of erythemato-squamous diseases.
• Hayes Roth dataset. This dataset [59] contains 5 numeric-valued attributes and 132 patterns.
• Heart dataset [60], used to detect heart disease.
• Ionosphere dataset. The ionosphere dataset contains data from the Johns Hopkins Ionosphere database and it has been studied in several papers [62,63].
• Liverdisorder dataset [64], used for detecting liver disorders in people using blood analysis.
• Mammographic dataset [65]. This dataset be used to identify the severity (benign or malignant) of a mammographic mass lesion from BI-RADS attributes and the patient’s age. It contains 830 patterns of 5 features each.
• PageBlocks dataset [66], used to detect the page layout of a document.
• Parkinsons dataset. This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson’s disease (PD) [67].
• Pima dataset [68], used to detect the presence of diabetes.
• Popfailures dataset [69], which is related to climate model simulation crashes of simulation crashes.
• Regions2 dataset. It is created from liver biopsy images of patients with hepatitis C [70]. From each region in the acquired images, 18 shape-based and color-based features were extracted, while it was also annotated by medical experts. The resulting dataset includes 600 samples belonging to 6 classes.
• Saheart dataset [71], used to detect heart disease.
• Segment dataset [72]. This database contains patterns from a database of 7 outdoor images (classes).
• Wdbc dataset [73], which contains data for breast tumors.
• Wine dataset, used to detect through chemical analysis the origin of wines and has been used in various research papers [74,75].
• Eeg datasets. As a real-world example, consider an EEG dataset described in [9] is used here. The dataset consists of five sets (denoted as Z, O, N, F and S) each containing 100 single-channel EEG segments each having 23.6 sec duration. With different combinations of these sets, the produced datasets are Z_F_S, ZO_NF_S and ZONF_S.
• ZOO dataset [76], where the task is to classify animals in seven predefined classes.
In addition, the following regression datasets were used:
• ABALONE dataset [77]. This dataset can be used to obtain a model to predict the age of abalone from physical measurements.
• AIRFOIL dataset, which is used by NASA for a series of aerodynamic and acoustic tests [78].
• BASEBALL dataset, a dataset to predict the salary of baseball players.
• BK dataset. This dataset comes from smoothing methods in statistics [79] and is used to estimate the points scored per minute in a basketball game.
• BL dataset: This dataset can be downloaded from StatLib. It contains data from an experiment on the effects of machine adjustments on the time to count bolts.
• CONCRETE dataset. This dataset is taken from civil engineering [80].
• DEE dataset, used to predict the daily average price of electricity energy in Spain.
• DIABETES dataset, a medical dataset.
• HOUSING dataset. This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University and it is described in [81].
• FA dataset, which contains percentage of body fat and ten body circumference measurements. The goal is to fit body fat to the other measurements.
• MB dataset. This dataset is available from smoothing methods in statistics [79] and it includes 61 patterns.
• MORTGAGE dataset, which contains the economic data information of the U.S.
• PY dataset (pyrimidines problem). The source of this dataset is the URL https://www.dcc.fc.up.pt/~ltorgo/Regression/DataSets.html (accessed on 23 May 2022) and it is a problem of 27 attributes and 74 patterns. The task consists of learning quantitative structure activity relationships (QSARs) and is provided by [82].
• QUAKE dataset. The objective here is to approximate the strength of an earthquake.
• TREASURY dataset, which contains economic data information of the U.S. from 1 April 1980 to 2 April 2000 on a weekly basis.
• WANKARA dataset, which contains weather information.

#### 3.2. Experimental Results

The method was compared against three other methods:
• A genetic algorithm with the same parameters that are shown in Table 1. In addition, after the termination of the genetic algorithm, the local search procedure of BFGS was applied to the best chromosome of the population, in order to enhance the quality of the solution. The column GENETIC in the experimental tables denotes the results from the application of this method.
• The Adam stochastic optimization method [83] as implemented in OptimLib, freely available from https://github.com/kthohr/optim (accessed on 23 May 2022). The results for this method are listed in the column ADAM in the relevant tables.
• The RPROP method [21] as implemented in the FCNN software package [84]. The results for this method are listed in the column RPROP in the relevant tables.
• The NEAT method (neuroevolution of augmenting topologies) [85] as implemented in the EvolutionNet package which is freely available from https://github.com/BiagioFesta/EvolutionNet (accessed on 23 May 2022). The maximum number of generations was the same as in the case of the genetic algorithm.
All the experiments were conducted 30 times with different seeds for the random number generator each time and averages were taken. To perform the experiments, the software IntervalGenetic is freely available from https://github.com/itsoulos/IntervalGenetic (accessed on 23 May 2022) was utilized. The experimental results for the classification datasets are shown in Table 2 and the results for the regression datasets are outlined in Table 3. For the classification problems, the average classification error on the test set is shown, and for regression datasets, the average mean squared error on the test set is displayed. In all cases, 10-fold cross validation was used and the number of hidden nodes (parameter H) was set to 10. The column DATASET stands for the name of the dataset incorporated, the column $D = 50$ represents the application of the proposed method with $D = 50$ as the initial value for the interval of weights, the column $D = 100$ stands for the results of the proposed method with $D = 100$ and finally the column $D = 200$ represents the results of the proposed method with $D = 200$. In both tables, an additional row was added at the end showing the average classification or regression error for all datasets and it is denoted by the name AVERAGE. All the experiments were conducted on an AMD Ryzen 5950X equipped with 128 GB of RAM. The operating system used was OpenSUSE Linux and all the programs were compiled using the GNU C++ compiler.
As can be seen from the experimental results, the proposed method is significantly superior to the other methods, especially in the case of regression data. The RPROP training method seems to overcome ADAM in most cases of classification datasets and the simple genetic method is better than ADAM and RPROP for classification datasets but not for regression datasets. In addition, the change in the parameter D does not seem to have a significant effect on the performance of the algorithm and the proposed algorithm achieves high performance even for small values of this parameter.
To make the dynamics of the proposed method clearer, another series of experiments was performed. In these, the maximum number of generations (parameter $N t$) received three values: 20, 40 and 100. For each value, all experiments for the classification and regression datasets were performed. The results for the classification datasets are listed in Table 4 and the results for the regression datasets are shown in Table 5. As expected, the proposed method improves its performance as the maximum number of generations increases, but even for a small number of generations it has a satisfactory performance.
In addition, to make a better and fairer comparison of the results, another set of experiments was performed with the genetic algorithm, in which the maximum number of generations was varied from 100 to 800, and the results are presented in Table 6 for the classification datasets and in Table 7 for the regression datasets. Observing these results, we can say that after 200 generations there is no significant difference in the efficiency of the genetic algorithm.

## 4. Conclusions

An innovative method of training artificial neural networks was presented in this paper. The method consists of two important phases: in the first phase, through a hybrid genetic algorithm, an attempt is made to identify the optimal interval of initialization and the training of the network parameters, and in the second phase, the training of the parameters in the optimal intervals of the first phase is performed using a genetic algorithm. The optimization of the optimal interval in the first phase is conducted by using partition rules for the initial interval which are applied in order. This technique aims to reduce the parameter search space and then significantly speed up network configuration training.
The proposed method was tested on a series of classification and regression datasets from the relevant literature and the experimental results seem to be very promising compared to the genetic algorithm procedure. However, since the method consists of two computational phases, it is much slower than other training techniques for artificial neural networks, and therefore, the use of parallel processing techniques is considered necessary.
Future improvements to the proposed method may include the incorporation of additional global optimization techniques instead of genetic algorithms, the usage of more advanced stopping rules and the application of the method to other types of neural networks such as radial basis function networks (RBF).

## Author Contributions

I.G.T., A.T. and E.K. conceived the idea and methodology and supervised the technical part regarding the software. I.G.T. conducted the experiments, employing several datasets, and provided the comparative experiments. A.T. performed the statistical analysis. E.K. and all other authors prepared the manuscript. E.K. and I.G.T. organized the research team and A.T. supervised the project. All authors have read and agreed to the published version of the manuscript.

## Funding

This research received no external funding.

Not applicable.

Not applicable.

Not applicable.

## Acknowledgments

The experiments of this research work were performed using the high-performance computing system established at Knowledge and Intelligent Computing Laboratory, Dept. of Informatics and Telecommunications, University of Ioannina, acquired with the project “Educational Laboratory equipment of TEI of Epirus” with MIS 5007094 funded by the Operational Programme “Epirus”, 2014–2020, by ERDF and national funds.

## Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Execution time comparison between the proposed algorithm and the other mentioned methods.
Figure 1. Execution time comparison between the proposed algorithm and the other mentioned methods.
Figure 2. Time comparison between the proposed method and a parallel implementation of Adam algorithm. The comparison is made for the dataset PageBlocks.
Figure 2. Time comparison between the proposed method and a parallel implementation of Adam algorithm. The comparison is made for the dataset PageBlocks.
Table 1. Experimental parameters.
Table 1. Experimental parameters.
PARAMETERVALUE
K20
H10
$N C$200
$N S$50
$N t$200
$P s$0.10
$P m$0.01
Table 2. Experiments for classification datasets.
Table 2. Experiments for classification datasets.
DATASETGENETICADAMRPROPNEAT$D = 50$$D = 100$$D = 200$
Appendicitis18.10%16.50%16.30%17.20%15.00%14.00%16.07%
Australian32.21%35.65%36.12%31.98%24.85%30.20%28.52%
Balance8.97%7.87%8.81%23.14%7.42%7.42%7.67%
Bands35.75%36.25%36.32%34.30%32.00%32.25%33.06%
Cleveland51.60%67.55%61.41%53.44%41.64%44.66%44.39%
Dermatology30.58%26.14%15.12%32.43%15.49%11.00%10.80%
Hayes Roth56.18%59.70%37.46%50.15%28.72%28.84%32.05%
Heart28.34%38.53%30.51%39.27%15.58%17.07%16.22%
Ionosphere15.14%16.64%13.65%19.67%12.25%9.71%7.12%
Liverdisorder31.11%41.53%40.26%30.67%30.90%29.54%30.70%
Lymography23.26%29.26%24.67%33.70%18.98%17.52%17.67%
Mammographic19.88%46.25%18.46%22.85%17.01%17.60%15.97%
PageBlocks8.06%7.93%7.82%10.22%7.73%7.01%6.71%
Parkinsons18.05%24.06%22.28%18.56%14.81%13.86%12.53%
Pima32.19%34.85%34.27%34.51%23.51%25.31%27.49%
Popfailures5.94%5.18%4.81%7.05%6.13%5.93%5.30%
Regions229.39%29.85%27.53%33.23%24.01%23.14%23.62%
Saheart34.86%34.04%34.90%34.51%28.94%29.04%29.93%
Segment57.72%49.75%52.14%66.72%47.38%49.49%40.61%
Wdbc8.56%35.35%21.57%12.88%6.23%5.28%5.49%
Wine19.20%29.40%30.73%25.43%5.51%6.55%6.22%
Z_F_S10.73%47.81%29.28%38.41%4.70%5.61%6.01%
ZO_NF_S8.41%47.43%6.43%43.75%5.39%4.67%5.81%
ZONF_S2.60%11.99%27.27%5.44%1.85%2.07%2.24%
ZOO16.67%14.13%15.47%20.27%14.83%11.40%8.50%
AVERAGE23.47%30.81%25.37%28.87%17.49%17.42%17.08%
Table 3. Experiments for regression datasets.
Table 3. Experiments for regression datasets.
DATASETGENETICADAMRPROPNEAT$D = 50$$D = 100$$D = 200$
ABALONE7.174.304.559.884.224.183.89
AIRFOIL0.0030.0050.0020.0670.0030.0030.003
BASEBALL103.6077.9092.05100.3949.4751.0753.57
BK0.0270.031.5990.150.0170.0170.019
BL5.740.284.380.050.00190.00160.0016
CONCRETE0.00990.0780.00860.0810.00530.00440.0042
DEE1.0130.630.6081.5120.1870.2050.203
DIABETES19.863.031.114.250.310.310.29
HOUSING43.2680.2074.3856.4919.2818.5017.75
FA1.950.110.140.190.0110.0120.012
MB3.390.060.0550.0610.0480.0470.047
MORTGAGE2.419.249.1914.110.570.700.53
PY105.410.090.0390.0750.0160.0140.014
QUAKE0.0400.060.0410.2980.0360.0360.036
TREASURY2.92911.1610.8815.520.4730.6770.622
WANKARA0.0120.020.00030.0050.00030.00020.0002
AVERAGE18.5511.7012.4412.704.674.744.81
Table 4. Experiments with $N t$ for the classification datasets.
Table 4. Experiments with $N t$ for the classification datasets.
DATASET$N t = 20$$N t = 40$$N t = 100$
Appendicitis15.23%15.37%15.77%
Australian32.85%33.15%30.18%
Balance11.92%7.61%8.71%
Bands35.61%33.86%32.96%
Cleveland43.91%43.35%41.29%
Dermatology28.41%21.28%14.33%
Hayes Roth50.33%38.56%36.80%
Heart20.61%21.16%19.99%
Ionosphere12.14%11.19%9.23%
Liverdisorder31.47%33.01%31.24%
Lymography22.24%22.57%20.74%
Mammographic18.66%17.37%15.71%
PageBlocks7.95%7.68%6.81%
Parkinsons17.28%17.44%13.86%
Pima33.19%31.94%30.71%
Popfailures6.65%5.81%5.24%
Regions226.33%26.03%22.25%
Saheart36.11%32.96%34.45%
Segment66.37%58.33%49.85%
Wdbc7.38%6.95%7.68%
Wine13.49%11.55%8.39%
Z_F_S7.77%7.59%8.38%
ZO_NF_S8.21%7.52%7.28%
ZONF_S2.26%1.87%1.99%
ZOO14.70%12.30%13.50%
AVERAGE22.12%20.41%18.88%
Table 5. Experiments with different values of $N t$ parameter for the regression datasets.
Table 5. Experiments with different values of $N t$ parameter for the regression datasets.
DATASET$N t = 20$$N t = 40$$N t = 100$
ABALONE4.884.774.63
AIRFOIL0.0040.0040.004
BASEBALL69.8365.3769.72
BK0.020.020.02
BL0.0060.0050.007
CONCRETE0.0080.0060.005
DEE0.2240.2250.199
DIABETES0.3570.3430.321
HOUSING26.4325.8820.65
FA0.0190.0190.017
MB0.050.050.05
MORTGAGE2.111.761.44
PY0.020.0180.022
QUAKE0.0420.0370.037
TREASURY2.372.121.48
WANKARA0.00040.00030.0003
AVERAGE6.656.296.16
Table 6. Experiments with the genetic method and various values of $N t$ for the classification datasets.
Table 6. Experiments with the genetic method and various values of $N t$ for the classification datasets.
DATASET$N t = 100$$N t = 200$$N t = 400$$N t = 800$
Appendicitis17.70%18.10%18.87%18.97%
Australian33.00%33.21%33.16%33.03%
Balance9.09%8.97%9.43%9.36%
Bands34.87%35.75%33.92%33.88%
Cleveland54.91%51.60%57.25%55.83%
Dermatology33.59%30.58%24.83%20.07%
Hayes Roth58.44%56.18%57.21%55.51%
Heart30.20%28.34%29.65%29.43%
Ionosphere14.69%15.14%10.02%9.84%
Liverdisorder33.30%31.11%33.24%33.19%
Lymography23.48%23.26%23.95%25.45%
Mammographic20.83%19.88%21.19%21.13%
PageBlocks8.28%8.06%8.04%7.42%
Parkinsons19.55%18.05%18.81%19.14%
Pima34.64%32.19%33.54%33.62%
Popfailures5.37%5.94%5.30%5.38%
Regions229.11%29.39%28.54%28.47%
Saheart35.25%34.86%34.60%34.93%
Segment56.07%57.72%52.43%51.00%
Wdbc9.08%8.56%9.02%9.19%
Wine30.43%19.20%25.35%21.55%
Z_F_S18.23%10.73%11.94%11.49%
ZO_NF_S16.61%8.41%10.85%10.09%
ZONF_S2.70%2.60%2.75%2.10%
ZOO16.37%16.67%13.47%13.33%
AVERAGE25.12%23.47%23.68%23.13%
Table 7. Experiments with the genetic method and various values of $N t$ for the regression datasets.
Table 7. Experiments with the genetic method and various values of $N t$ for the regression datasets.
DATASET$N t = 100$$N t = 200$$N t = 400$$N t = 800$
ABALONE6.887.176.286.49
AIRFOIL0.0080.0030.040.01
BASEBALL106.47103.60107.04107.30
BK0.650.0270.0380.097
BL9.805.741.382.85
CONCRETE0.0170.010.290.42
DEE0.361.010.480.25
DIABETES38.0419.8613.7013.50
HOUSING38.4443.2636.5135.81
FA1.551.950.742.06
MB0.613.391.130.62
MORTGAGE2.122.411.941.84
PY151.49105.4196.7990.59
QUAKE0.220.040.050.04
TREASURY2.722.932.282.19
WANKARA0.0650.0120.0010.003
AVERAGE22.4718.5516.7416.51
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Tsoulos, I.G.; Tzallas, A.; Karvounis, E. A Rule-Based Method to Locate the Bounds of Neural Networks. Knowledge 2022, 2, 412-428. https://doi.org/10.3390/knowledge2030024

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Tsoulos IG, Tzallas A, Karvounis E. A Rule-Based Method to Locate the Bounds of Neural Networks. Knowledge. 2022; 2(3):412-428. https://doi.org/10.3390/knowledge2030024

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Tsoulos, Ioannis G., Alexandros Tzallas, and Evangelos Karvounis. 2022. "A Rule-Based Method to Locate the Bounds of Neural Networks" Knowledge 2, no. 3: 412-428. https://doi.org/10.3390/knowledge2030024