=Paper=
{{Paper
|id=Vol-1498/HAICTA_2015_paper76
|storemode=property
|title=Investigation of Dairy Cattle Traits by Using Artificial Neural Networks and Cluster Analysis
|pdfUrl=https://ceur-ws.org/Vol-1498/HAICTA_2015_paper76.pdf
|volume=Vol-1498
|dblpUrl=https://dblp.org/rec/conf/haicta/AtilA15
}}
==Investigation of Dairy Cattle Traits by Using Artificial Neural Networks and Cluster Analysis==
https://ceur-ws.org/Vol-1498/HAICTA_2015_paper76.pdf
Investigation of Dairy Cattle Traits by Using Artificial
Neural Networks and Cluster Analysis
Hülya Atıl1, Asli Akilli2
1
Department of Biometry and Genetic, Faculty of Agriculture, Ege University,
Izmir, Turkey
2
Department of Biometry and Genetic, Faculty of Agriculture, Ahi Evran University, Kirsehir,
Turkey
Abstract. Artificial neural networks is a method which based on artificial
intelligence, has been emerged according to the working principles of the
human brain nerve cells. Especially in the modelling of nonlinear systems,
with the information learned through experience similarly to humans, it
provides classification, pattern recognition, optimization and allows the
realization of forward-looking forecasts. Artificial neural networks is very
successful method that has been the subject of many studies in different
disciplines. Artificial neural network studies performed in animal husbandry in
recent years, often located in the literature such as prediction of yield
characteristics and classification, animal breeding, quality assessment, disease
diagnosis. In this study, classification according to some traits of dairy cattle
using artificial neural networks and k-means method are aimed. Due to results
of the research, it is determined that artificial neural networks is more
successful than the k-means clustering method. The analysis of study was
performed using SPSS 20.0 statistical software package and Matlab R2011b
work programme.
Keywords: Classification, artificial neural network, dairy cattle.
1 Introduction
One of the key issues in the field of animal breeding studies is classification
differences and similarities measure with respect to each other to various
characteristics of the animals. The animals’ correct classification which made by the
yield property or exterior features provide great advantages manufacturer and
farmers.
Conscious breeding work, to be derived from animals is to maximize the level of
economic benefits expected in herd management, which plays an important role in
maintaining a profitable production. The aim of classification of animals is getting
homogeneous in itself, themselves a heterogeneous group. Some of the statistical
methods are known as cluster analysis, fuzzy clustering analysis, fuzzy logic, neural
networks, and data mining methods used to make classification or clustering for this
purpose in farming.
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Artificial neural network method is one of the performing machine learning
methods based on artificial intelligence. Problems encountered in daily life vary
under different conditions. This parallel opposite problem, combined with advances
in technology, new methods of solution are produced. Artificial intelligence
technologies provide great benefits to the people across the problem way to find a
solution on the basis of the system created. Although it is fairly widely used,
especially engineering, medicine, agriculture areas etc. artificial neural network is
frequently used in recent years. Artificial neural networks is a method that have
emerged to move the workings of nerve cells in the human brain. People likewise, it
can perform functions such as optimization, prediction, classification, pattern
recognition using information learned through experience based on the data. It is
very successful method that located in linear and non-linear system the relationship
between input and output variables mathematical modelling, in many cases according
to conventional statistical methods with low error rates obtained from the analysis
results (Takma et al. 2012; Akıllı and Atıl 2014).
In animal science artificial neural network is successfully applied such as
diagnosis of diseases such as mastitis and lameness (Yang ve ark. 1999; Cavero et al.
2008; Sun 2008; Hassan et al. 2009; Roush et al. 2001), in the prediction of the
forward-looking traits (Grzesiak et al. 2003; Salehi et al. 1998; Sanzogni and Kerr
2001; Kominakis et al. 2002; Hosseinia et al. 2007; Görgülü 2012), animal breeding
studies (Shahinfar et al. 2012; Salehi et al. 1997; Grzesiak et al. 2010), in the
prediction of the nutrient content in manure (Chen et al. 2008; Chen et al. 2009;
Chen et al. 2009) and oestrus detection (Krieter et al. 2006).
In this study, it is aimed using artificial neural networks and k-means clustering
method in the framework of the identified variables divided into homogeneous
groups of dairy cattle.
2 Material and Method
2.1 Material
The study material consists of records related to 10,000 head of Holstein Friesian
dairy cattle for milk, fat and protein yield values with calving interval, age at first
calving, milking days and season variables that obtained between 1981-2000 years.
Data analysis was carried out using SPSS 20.0 statistical software package
programme and MATLAB (R2011b) programme.
2.2 Method
In this study, artificial neural network and k-means clustering method which is
one of multivariate statistical methods are used for classification according to the
specified characteristics of dairy cattle.
Cluster analysis provides in itself homogeneous and between them heterogeneous
group separation unable to obtain precise information about the studied data group
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data sets taking into consideration the differences and similarities in that they showed
against each other. Cluster analysis is being implemented in three phases such as
creation of data matrix, selection of the clustering technique and discussion of the
significance of the results (Tatlıdil 1996; Alpar 2011).
In this study, k-means method is used which is one of non-hierarchical clustering
methods. In this technique, it is not necessary to determine the similarity and distance
matrix. However, it should be noted by the researchers of a possible cluster number
(Özdamar 2010). Standardized data variables are used because of having different
scale and size value the variables examined in the study. The data is converted to a
standard value Z scores [𝑍 ∼ 𝑁 0,1 ]. Formula used for this purpose is located in
Equation 1 (Kalaycı, 2008).
(!!!)
𝑧= (1)
!
In k-means technique, observations divided into k clusters including the smallest
sum of squares within clusters as shown in Equation 2.
1 n min 2
Wn = ∑ xi − a jn
n i =1 1 ≤ j ≤ k (2)
According to the rule the observation of classification takes place in the nearest
cluster. In the rule; each one x1, x2,...,xn determined number of variables for each
observation vector set, a1, a2.....akn each group were selected as cluster centres for
individuals (Tatlıdil, 1996).
The second method is a method used in the study neural networks. Artificial
neuron with a similar operating principle biological nerve cells form a neural
network together. Artificial neuron consists of different numbers of inputs and
outputs. Inputs pass through the activation function with a sum function to collect
information from the outside and generate output. The generated output is transferred
to another cell via links to have the neural network (Öztemel, 2006; Negnevitsky,
2001). The comparative view of the biological and neural cells is located in Table 1.
Table 1. Biological neuron and artificial neuron
Biological
Explanation Artificial Neuron
Neuron
Neuron Nerve cell Processor element
Axons One-way transmission of information Outputs
Dendrites Receiving the information Inputs
Synapse Communication between nerve cells Weights
Artificial neural networks, is composed of input- output layer and one or different
number of hidden layers in between the layers. Received data from the input layer is
processed in the hidden layer and then sent to the output layer. Determining the
weight value concerning the training of the network that allows nerve cells in neural
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networks is an important process. Severity of incoming data is decisive here.
Determining the proper amount of weight, it means to make accurate generalizations
to do right never seen through examples and thereby create new information. The
data of the input variables are converted to output values in the output layer being
associated with weight values (Negnevitsky, 2001; Baykal and Beyan, 2004;
Öztemel, 2006; Russel and Norvig, 2010; Uğur and Kınacı, 2006).
The studies related to neural networks have led to the emergence of different
network structures according to the different problem structure. In the literature, the
most commonly used networks single and multilayer perceptrons, vector quantization
models (LVQ), self organized map (SOM), adaptive resonance theory (ART),
Hopfield network, Elman network, radial basis function network (Öztemel, 2006).
Artificial neural networks are classified as feed forward networks, and feedback
networks depending on the direction of information flow. Also it is classified
according to different learning strategies such as supervised, unsupervised and
reinforcement learning. View of the multi-layered artificial neural network is located
in Figure 1.
Fig. 1. Multilayer perceptron artificial neural network.
This neural network model used in this study is defined as multilayer perceptron.
Multi-layered sensor model operates according to the supervised learning strategy
and it has feed forward terms of the direction of flow of information structure. This
learning algorithm used in the multilayer perceptron model is designed to work as
back-propagation algorithm. Back propagation algorithm operation is performed in
two basic stages. The data of the input variables in the first stage is presented to the
neural network, in next stage after the processing of data weight in each layer based
on the error level is updated on the resulting output and in order to minimize error,
the spread of error is provided backwards from the output layer. Levenberg-
Marquardt algorithm, which is the back propagation algorithm, the problem structure
in this study to investigate suitability, is preferred because the learning error and low
running faster than other algorithms.
The number of processor elements in each layer and number of layers in the
network structure acts in substantially the operating performance of artificial neural
networks. In literature, a specific formula for determining the number of layers and
the processor element is not included. In this study, the numbers of processor
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elements and layer were determined using detailed literature study for research and
trial and error with the heuristic.
Perform the clustering process with the lowest error which layer and the processor
element number is determined as the optimal number. Performance criteria used for
this, it was determined as the coefficient of determination (R2), the root mean square
error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error
(MAPE). According to these criteria with high value R2 and error variance
expressing the low value of RMSE, MAD and MAPE statistics is the low value
indicated that the best fit of the model which is the subject of research. R2, RMSE,
MAD and MAPE for the calculation of statistical formulas is located in respectively
∧
Equation 3-6. In equations n: number of records, Yi: observed value, Yi : predicted
value.
n ∧
∑ (Yi − Y )2
R 2 = i=1
n (3)
∑ (Y − Y ) i
2
i=1
n ∧
∑ (Y − Y ) i
2
RMSE = i=1 (4)
n
n ∧
∑ Y −Y i i
MAD = i=1 (5)
n
∧
Y −Y
n
∑ iY i
i=1 i
MAPE = × 100 (6)
n
The data are subjected to normalization pre-processing prior to analysis done by
the method of artificial neural networks. Normalization process network to reduce
the difficulties during the training is done in order to run faster in the training process
of the network and the balancing of the importance of the parameters involved in
research. In the literature different types of normalization techniques are located.
Researchers are different techniques depending on the problem structure may choose
(Öztemel 2006; Xu et al, 2007; Jayalakshmi and Santhakumaran, 2011). In this study
data [0.1-0.9] range is scaled using the formula in the Equation 7 (Xu et al, 2007).
(P − Pmin ) (7)
Pn = 0.1 + (0.9 − 0.1) ×
(Pmax − Pmin )
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In Equation 7, Pn contained in the data set represents the normalized value as a value
P. Pmax and Pmin , located in the input or output variable indicates the data set
having the highest and lowest values of the data. After the normalization process is
complete the minimum value of 0.1 and the maximum value of 0.9 is determined in
the data set. After network training is completed and the test output data obtained to
convert the actual scale of the output, the formula was used in the Equation 8. In next
step, made a comparison between the predicted value and actual value and
performance criteria is calculated.
(Pmax − Pmin ) (8)
P = (Pn − 0.1) × + Pmin
(0.9 − 0.1)
P represents the converted value that is transformed pre-normalization.
Before starting the analysis with neural networks dataset completely randomized
on the 80% training and 20% testing data set is divided into two different groups
namely. 8,000 head of dairy cattle in the training data set and test data set consists of
records pertaining to 2,000 head of dairy cattle milk production traits variable.
In this study, a detailed literature review for research and using trial and error,
layers and the number of processor elements are determined. Prediction performs
operations with the lowest error layer and the number of processor elements has been
identified as the most appropriate number. Number of hidden layers is “1” and the
number of neuron in this layer is “7” are determined in the neural network designed
study. Also, convergence criteria and maximum number of iteration has been
recognized as 1x10-6 and 1000. Animals which have been the subject of research
were clustered before analysis by an expert. The methods’ performance was
evaluated with the expert opinion. The inputs of model are defined as first calving
age model, calving interval, milk with milking day data, the value related to fat and
protein yield. The output of the model refers to the group to which it belongs to the
subject of dairy cattle research.
3 Results and Discussion
In this study, artificial neural networks, k-means clustering method based on
10,000 head of dairy cattle milk production traits is divided into homogeneous
groups. In the result of analysis, animals were included in three different groups.
Analysis of the results of artificial neural networks is located in Table 2. According
to the results of highest classification success in the case where the number of
neurons is provided seven. Here, the neural network which has seven neurons is
observed that coefficient of determination of 99.9% and lower levels of error
statistics according to the number of other neurons.
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Table 2. Artificial neural network test results working with different numbers of neuron.
Neuron
R2 RMSE MAD MAPE
Number
4 99.9% 1.7603 0.08115 0.0048
5 99.9% 1.7603 0,08114 0.0017
6 99.9% 1.7601 0,08113 0.0106
7 99.9% 1.7599 0,08108 0.0177
8 99.7% 1.7595 0,08104 0.0088
9 99.8% 1.7594 0,08103 0.0513
10 99.9% 1.7602 0.08114 0.0053
The neural network and k-means method is situated classification performance in
Table 3. The method used in the classification process seems to be quite successful
relating to dairy cattle. The results indicate that the more successful classification by
the k-means method of artificial neural network method.
Table 3. Artificial neural network and K-means results.
Statistics ANN K-means
Coefficient of
99.9% 94.9%
determination
RMSE 1.7599 1.7758
MAD 0,08108 0.0002
MAPE 0.0177 0.1048
Artificial neural network and k-means method is located performance values of
conformational display in Figure 2.
Classification works done by artificial neural networks are quite common in the
livestock area. Hassan et al. (2009) a neural network model used for the detection of
mastitis and analysis resulted in success in their study. A like, Yang et al. (2000) in
the estimation of clinical mastitis cases with milk production traits studied the
availability of artificial neural networks. The study of comparative review of
artificial neural networks, Grzesiak et al. (2003) in their study, multiple regression
and artificial neural networks methods have used to estimate the 305-day lactation
milk yield. Takma et al. (2012), lactation milk yield of Holstein were modelled using
multiple regression and neural network. In both studies it noted that the comparison
of the results artificial neural networks can be an alternative method to regression
analysis.
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ANN
200,00%
150,00%
100,00%
50,00%
0,00%
Coefficient of RMSE MAD MAPE
Determination
K-‐Mean
200,00%
150,00%
100,00%
50,00%
0,00%
Coefficient of RMSE MAD MAPE
Determination
Fig. 2. Artificial neural network and K-means method results
In this study, artificial neural networks and k-means clustering methods were
examined in comparison to their classification and such as mentioned in recent
studies methods artificial neural networks that provide very good results.
4 Conclusion
In this study, k-means clustering method and the method of artificial neural
networks are used for classification of dairy cattle. When the k-means clustering
method and artificial neural network method of classification performance
comparison, the neural network is seen as a better fit. The survey results indicate
neural networks can be used as an alternative to clustering analysis of animal science
methods. Artificial neural network -the machine learning perform and one of the
artificial intelligence methods- provide a lower estimate convenience incorrect
classification according to the traditional method for researchers. Classification work
done by artificial neural networks in the field of animal science in the literature is
quite low. In the later stages of this study it is intended to be resources for individuals
to do research on this subject. Different neural network models and different input
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variables with in studies are expected to give positive results in the classification of
animals.
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