PROS Maria FVR, Brodjol SS U Classifying the poor fulltext
Proceedings of the IConSSE FSM SWCU (2015), pp. MA.66–70
MA.66
ISBN: 978-602-1047-21-7
Classifying the poor household using neural network
Maria F.V. Ruslau* and Brodjol Sutijo S. Ulama
Department of Statistics, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia
Abstract
The issue of poverty has recently been brought to the public’s attention. The picture of
Indonesian development reveals many families are not benefiting from national
economic growth. Many families were still poor and hovering below the poverty line. The
classification of the individual or of poor households in a class or poverty status can be a
good instrument to focus on the living conditions of the poor. In this study, back
propagation algorithm was used to build models of neural networks that can classify each
poor household appropriate their poverty status. Network is built using the weights of
the selection of the best network. The best networks have been training on the sub-sub
smaller dataset. Classification is done by replication 10-fold cross validation. Average
accuracy of classification in the training data is 58.89 percent while the testing data of
56.42 percent.
Keywords back propagation, classification, neural network, poverty
1.
Introduction
Classification is one of the most frequently encountered decision making tasks of
human activity and application areas of neural networks. A classification problem occurs
when an object needs to be assigned into a predefined group or class based on a number of
observed attributes related to that object. Many problems in business, science, industry, and
medicine can be treated as classification problems. Examples include bankruptcy prediction,
credit scoring, medical diagnosis, quality control, handwritten character recognition, and
speech recognition (TNP2K, 2013a).
Many social studies analyze attitude responses using regression models. The
classification and recognition of individual characteristics and behaviours constitute a
preliminary step and is an important objective in the behavioural sciences (TNP2K, 2013b).
Current statistical methods do not always give satisfactory results. Neural network are an
alternative method for classification can work with large numbers of qualitative variables
such as behaviours, provided that they can be coded, and they are able to use non-linear
linked variables. A methodology based on one of the principles of artificial neural networks,
the backpropagation, can improve performance in this area (Boonkiatpong & Sinthupinyo,
2011).
This research focus on the analysis with large data of poor household by applying
backpropagation neural networks. Measurement and analysis of poverty is necessary to
identify individuals and households in need of government assistance and aid. Cases in this
study was a multi-class classification for ordinal response or target. To analyze these data we
*
Corresponding author. Tel.: +682335111988; E-mail address: mariafvruslau@yahoo.com
SWUP
M.F.V. Ruslau, B.S.S. Ulama
MA.67
adopt the techniques introduced by Boonkiatpong & Sinthupinyo (2011). Classification
accuracy is not increased significantly. However, the network integration method, simply
helping to reduce errors and improve accuracy, especially for large and heterogeneous data
sets.
2.
Materials and methods
2.1 Back propagation model
Back propagation is a learning technique that adjusts weights in the neural network by
propagating weight changes. It is simply a gradient descent method to minimize the total
square error of the output computed by the network (Cherkassky & Mulier, 2007). The back
propagation is one of the simplest and most general methods for supervised training of
multilayer neural networks, in which neurons connect each other with weighted connection.
Back propagation has become popular to classification problem in recent years. Back
propagation algorithm can be developed to multi classification problem. Multi classification
problem has similarities to binary classification, which makes the network structure then
randomize the weight and calculate the output unit. The difference lies in the calculation
process for the case multi category output where the output will work in accordance with
the classification process, namely calculation of output is processed through back
propagation learning algorithm with binary numbers, if the output goes into classification
class, then the class contains a value of 1, whereas a class apart It contains the value 0.
Zk
Xi
Wik
Vkj
Yj
Output Layer
Input Layer
Hidden Layer
Figure 1. Backpropagation architecture for multi classification category.
The architecture consists of one input layer, one hidden layer and one output layer. In thus
case, Xi is the i-th input variable, Wik is the weight of the i-th input node to node k-th in the
hidden layer. Zk is k-th node in the hidden layer. Yj is the j-th output node, the number of
categories and Vkj is the weight of node k-th in the hidden layer to the j-th node in the output
layer.
2.2 Implementation procedure
In this study, we are predicting the class (classification) of an outcome variable.
Response in this study consisted of four categories. The response variable was the poverty
status. This situation is identical to the multi classification learning process, its means the
binary process n times. The predictors and response variables will be scaled to [0,1] scale
before entering them into the network.
SWUP
Classifying the poor household using neural network
MA.68
Our experiment work with a large dataset of poor household, 65.658 households with
16 predictor variables. Consists of one ordinal variables, three interval variables, and
nominals. Thus, we divide the original training set (N0) into n sub data sets, namely N1 to Nn.
Each sub dataset will be trained by the same Back propagation network structure with a
single hidden layer. The number of nodes in hidden layer was selected by training with
different number of hidden layer. The result shown in Figure 2. and we were collected the
weight from each node in the lowest error network and use these weights as weight to
training networks in original dataset with the same structure.
3.
Results and discussion
We started working with determines the number of nodes in hidden layer which is
appropriate for this case. According to Figure 2, we can see that, increasing the number of
nodes in the hidden layer increases the accuracy of the training data, however, large number
of nodes were not always improved the accuracy of testing data, it is shown in Figure 3.
Figure 2. Accuracy levels of training and testing dataset based on number of node in
hidden layer.
Figure 3. Mean square error based on number of node in hidden layers.
Based on the results in Figure 2 and Figure 3, the neural network model used for
training in this case was consist of an input layer with 38 nodes, one hidden layer with 35
nodes and 4 nodes in the output layer. The architecture is shown in Figure 4.
The accuracy of sub datasets from the datasets is shown in Table 1. The N26 is the best
network of data. Furthermore, the weights of N26 used to training the network by using
original dataset. The results showed that the best network was a Nbest with lowest of error.
The weight from this network was used to replace the trained weight of original dataset with
the same structure.
SWUP
M.F.V. Ruslau, B.S.S. Ulama
MA.69
Figure 4. The Back propagation training network.
Dataset
Nall
N1
N2
N3
N4
N5
N6
N7
N8
N9
Accuracy
58.57
76.45
76.20
74.90
76.75
77.15
77.90
77.25
75.50
76.00
Table 1. MSE and accuracy of each network.
MSE
Dataset Accuracy
MSE
Dataset
0.13196
N10
77.15
0.09066
N20
0.09517
N11
78.40
0.09222
N21
0.09538
N12
76.25
0.09258
N22
0.09756
N13
76.90
0.09262
N23
0.09252
N14
77.85
0.09067
N24
0.09194
N15
76.95
0.08918
N25
0.08834
N16
76.60
0.09254
N26
0.09126
N17
77.50
0.08995
N27
0.09287
N18
77.80
0.08892
N28
0.09289
N19
76.05
0.09239
Accuracy
77.1
77.65
77.00
76.90
79.35
78.20
79.4
79.00
79.80
MSE
0.08892
0.09171
0.08971
0.09013
0.08482
0.08817
0.08322
0.08548
0.08367
Training with the original datasets showed a poor classification accuracy. The average
accuracy of classification generated from training backpropagation network is 58.89 percent.
Accurately, accuracy of the classification for each category that generated the highest in
categories 1 (one), the second with a high degree of accuracy is in category 3 (three), then
the category 2 (two) and the last with the lowest level of classification accuracy is category 4
(four). Average accuracy of data classification testing is very low at 56.42 percent. This is
because the data used in our experiment was imbalanced dataset in category. Thus, most of
the data is classified into the majority categories. The result of training and testing showed
the same pattern of classification.
4.
Conclusion and remarks
By using neural network, increasing the number of nodes in the hidden layer was
improve the performance of classification. The weights trained from sub dataset did not
achieve more better result when they were tested on the original dataset, even though there
was an increasing accuracy and decreasing of errors. On large dataset, backpropagation does
not work much better than small dataset to achieve a good classification, especially for
SWUP
Classifying the poor household using neural network
MA.70
highly variations of dataset and for imbalanced dataset. A technique is needed to handling
imbalanced dataset and manage large variations in input before it is inserted into the
network to trained.
Tabel 2. The classification accuracy of 10-fold cross validation backpropagation network.
Accuracy
Fold
Training(%)
Testing(%)
1
59,11
56,41
2
59,03
56,30
3
59,20
52,01
4
59,11
53,64
5
58,72
58,14
6
58,86
56,54
7
58,28
61,93
8
58,76
58,10
9
58,83
56,76
10
59,04
54,39
References
TNP2K (2013a). Pembangunan basis data terpadu. Jakarta: TNP2K.
TNP2K (2013b). Buku tanya jawab basis data terpadu untuk program perlindungan sosial. Jakarta:
TNP2K.
Boonkiatpong, K., & Sinthupinyo, S. (2011). Applying multiple neural network on large scale data.
International Proceedings of Computer Science and Information Technology, Singapore, 6, 189–
193.
Cherkassky, V., & Mulier, F. (2007). Learning from data: Concepts, theory, and methods (2nd ed.). New
Jersey: John Wiley & Sons.
SWUP
MA.66
ISBN: 978-602-1047-21-7
Classifying the poor household using neural network
Maria F.V. Ruslau* and Brodjol Sutijo S. Ulama
Department of Statistics, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia
Abstract
The issue of poverty has recently been brought to the public’s attention. The picture of
Indonesian development reveals many families are not benefiting from national
economic growth. Many families were still poor and hovering below the poverty line. The
classification of the individual or of poor households in a class or poverty status can be a
good instrument to focus on the living conditions of the poor. In this study, back
propagation algorithm was used to build models of neural networks that can classify each
poor household appropriate their poverty status. Network is built using the weights of
the selection of the best network. The best networks have been training on the sub-sub
smaller dataset. Classification is done by replication 10-fold cross validation. Average
accuracy of classification in the training data is 58.89 percent while the testing data of
56.42 percent.
Keywords back propagation, classification, neural network, poverty
1.
Introduction
Classification is one of the most frequently encountered decision making tasks of
human activity and application areas of neural networks. A classification problem occurs
when an object needs to be assigned into a predefined group or class based on a number of
observed attributes related to that object. Many problems in business, science, industry, and
medicine can be treated as classification problems. Examples include bankruptcy prediction,
credit scoring, medical diagnosis, quality control, handwritten character recognition, and
speech recognition (TNP2K, 2013a).
Many social studies analyze attitude responses using regression models. The
classification and recognition of individual characteristics and behaviours constitute a
preliminary step and is an important objective in the behavioural sciences (TNP2K, 2013b).
Current statistical methods do not always give satisfactory results. Neural network are an
alternative method for classification can work with large numbers of qualitative variables
such as behaviours, provided that they can be coded, and they are able to use non-linear
linked variables. A methodology based on one of the principles of artificial neural networks,
the backpropagation, can improve performance in this area (Boonkiatpong & Sinthupinyo,
2011).
This research focus on the analysis with large data of poor household by applying
backpropagation neural networks. Measurement and analysis of poverty is necessary to
identify individuals and households in need of government assistance and aid. Cases in this
study was a multi-class classification for ordinal response or target. To analyze these data we
*
Corresponding author. Tel.: +682335111988; E-mail address: mariafvruslau@yahoo.com
SWUP
M.F.V. Ruslau, B.S.S. Ulama
MA.67
adopt the techniques introduced by Boonkiatpong & Sinthupinyo (2011). Classification
accuracy is not increased significantly. However, the network integration method, simply
helping to reduce errors and improve accuracy, especially for large and heterogeneous data
sets.
2.
Materials and methods
2.1 Back propagation model
Back propagation is a learning technique that adjusts weights in the neural network by
propagating weight changes. It is simply a gradient descent method to minimize the total
square error of the output computed by the network (Cherkassky & Mulier, 2007). The back
propagation is one of the simplest and most general methods for supervised training of
multilayer neural networks, in which neurons connect each other with weighted connection.
Back propagation has become popular to classification problem in recent years. Back
propagation algorithm can be developed to multi classification problem. Multi classification
problem has similarities to binary classification, which makes the network structure then
randomize the weight and calculate the output unit. The difference lies in the calculation
process for the case multi category output where the output will work in accordance with
the classification process, namely calculation of output is processed through back
propagation learning algorithm with binary numbers, if the output goes into classification
class, then the class contains a value of 1, whereas a class apart It contains the value 0.
Zk
Xi
Wik
Vkj
Yj
Output Layer
Input Layer
Hidden Layer
Figure 1. Backpropagation architecture for multi classification category.
The architecture consists of one input layer, one hidden layer and one output layer. In thus
case, Xi is the i-th input variable, Wik is the weight of the i-th input node to node k-th in the
hidden layer. Zk is k-th node in the hidden layer. Yj is the j-th output node, the number of
categories and Vkj is the weight of node k-th in the hidden layer to the j-th node in the output
layer.
2.2 Implementation procedure
In this study, we are predicting the class (classification) of an outcome variable.
Response in this study consisted of four categories. The response variable was the poverty
status. This situation is identical to the multi classification learning process, its means the
binary process n times. The predictors and response variables will be scaled to [0,1] scale
before entering them into the network.
SWUP
Classifying the poor household using neural network
MA.68
Our experiment work with a large dataset of poor household, 65.658 households with
16 predictor variables. Consists of one ordinal variables, three interval variables, and
nominals. Thus, we divide the original training set (N0) into n sub data sets, namely N1 to Nn.
Each sub dataset will be trained by the same Back propagation network structure with a
single hidden layer. The number of nodes in hidden layer was selected by training with
different number of hidden layer. The result shown in Figure 2. and we were collected the
weight from each node in the lowest error network and use these weights as weight to
training networks in original dataset with the same structure.
3.
Results and discussion
We started working with determines the number of nodes in hidden layer which is
appropriate for this case. According to Figure 2, we can see that, increasing the number of
nodes in the hidden layer increases the accuracy of the training data, however, large number
of nodes were not always improved the accuracy of testing data, it is shown in Figure 3.
Figure 2. Accuracy levels of training and testing dataset based on number of node in
hidden layer.
Figure 3. Mean square error based on number of node in hidden layers.
Based on the results in Figure 2 and Figure 3, the neural network model used for
training in this case was consist of an input layer with 38 nodes, one hidden layer with 35
nodes and 4 nodes in the output layer. The architecture is shown in Figure 4.
The accuracy of sub datasets from the datasets is shown in Table 1. The N26 is the best
network of data. Furthermore, the weights of N26 used to training the network by using
original dataset. The results showed that the best network was a Nbest with lowest of error.
The weight from this network was used to replace the trained weight of original dataset with
the same structure.
SWUP
M.F.V. Ruslau, B.S.S. Ulama
MA.69
Figure 4. The Back propagation training network.
Dataset
Nall
N1
N2
N3
N4
N5
N6
N7
N8
N9
Accuracy
58.57
76.45
76.20
74.90
76.75
77.15
77.90
77.25
75.50
76.00
Table 1. MSE and accuracy of each network.
MSE
Dataset Accuracy
MSE
Dataset
0.13196
N10
77.15
0.09066
N20
0.09517
N11
78.40
0.09222
N21
0.09538
N12
76.25
0.09258
N22
0.09756
N13
76.90
0.09262
N23
0.09252
N14
77.85
0.09067
N24
0.09194
N15
76.95
0.08918
N25
0.08834
N16
76.60
0.09254
N26
0.09126
N17
77.50
0.08995
N27
0.09287
N18
77.80
0.08892
N28
0.09289
N19
76.05
0.09239
Accuracy
77.1
77.65
77.00
76.90
79.35
78.20
79.4
79.00
79.80
MSE
0.08892
0.09171
0.08971
0.09013
0.08482
0.08817
0.08322
0.08548
0.08367
Training with the original datasets showed a poor classification accuracy. The average
accuracy of classification generated from training backpropagation network is 58.89 percent.
Accurately, accuracy of the classification for each category that generated the highest in
categories 1 (one), the second with a high degree of accuracy is in category 3 (three), then
the category 2 (two) and the last with the lowest level of classification accuracy is category 4
(four). Average accuracy of data classification testing is very low at 56.42 percent. This is
because the data used in our experiment was imbalanced dataset in category. Thus, most of
the data is classified into the majority categories. The result of training and testing showed
the same pattern of classification.
4.
Conclusion and remarks
By using neural network, increasing the number of nodes in the hidden layer was
improve the performance of classification. The weights trained from sub dataset did not
achieve more better result when they were tested on the original dataset, even though there
was an increasing accuracy and decreasing of errors. On large dataset, backpropagation does
not work much better than small dataset to achieve a good classification, especially for
SWUP
Classifying the poor household using neural network
MA.70
highly variations of dataset and for imbalanced dataset. A technique is needed to handling
imbalanced dataset and manage large variations in input before it is inserted into the
network to trained.
Tabel 2. The classification accuracy of 10-fold cross validation backpropagation network.
Accuracy
Fold
Training(%)
Testing(%)
1
59,11
56,41
2
59,03
56,30
3
59,20
52,01
4
59,11
53,64
5
58,72
58,14
6
58,86
56,54
7
58,28
61,93
8
58,76
58,10
9
58,83
56,76
10
59,04
54,39
References
TNP2K (2013a). Pembangunan basis data terpadu. Jakarta: TNP2K.
TNP2K (2013b). Buku tanya jawab basis data terpadu untuk program perlindungan sosial. Jakarta:
TNP2K.
Boonkiatpong, K., & Sinthupinyo, S. (2011). Applying multiple neural network on large scale data.
International Proceedings of Computer Science and Information Technology, Singapore, 6, 189–
193.
Cherkassky, V., & Mulier, F. (2007). Learning from data: Concepts, theory, and methods (2nd ed.). New
Jersey: John Wiley & Sons.
SWUP