A Novel Approach in Determining Neural Networks Architecture to Classify Data With Large Number of Attributes

Rachmatullah, Muhammad Ibnu Choldun; Santoso, Judhi; Surendro, Kridanto

doi:10.1109/access.2020.3036853

Cited by 13 publications

(18 citation statements)

References 30 publications

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“…This topology that uses two hidden layers requires a cumulative variance of 99%, so that the selected topology will be compared with the topology of other researchers. This also proves that the cumulative variance PCA required for the regression objective function is greater than the classification objective function as has been done in previous research: for multi-class classification it needs a PCA cumulative variance of about 70% ( Ibnu Choldun, Santoso & Surendro, 2020 ; Rachmatullah, Santoso & Surendro, 2020 ) while for binary classification it needs a PCA cumulative variance of about 40% ( Rachmatullah, Santoso & Surendro, 2020 ).…”

Section: Resultssupporting

confidence: 80%

“…However, increasing the number of neurons using only one hidden layer does not always guarantee an increase in performance, such as the performance of Madhiarasan (44) which is lower than the topology that uses two hidden layers with fewer neurons, the Tamura & Tateishi (5.5) topology and the proposed topology (3.10). This study also shows that the the cumulative variance for the regression objective function, in this study 99% greater than the cumulative variance for the classification objective function in previous studies ( Rachmatullah, Santoso & Surendro, 2020 ), where for binary classification needs a PCA cumulative variance of 38.9%, while the multi-class classification needs a PCA cumulative variance of 69.7%.…”

Section: Resultssupporting

confidence: 49%

“…The clustering application is carried out using the K-means method, while to determine the optimal cluster number using the modified Elbow criteria. The optimal clusters number has been obtained based on the wss value, that is, when the wss value in a row (at least three in a row) has remained relatively unchanged ( Rachmatullah, Santoso & Surendro, 2020 ). The examples of applying the modified Elbow criteria are shown in Table 4 , which is the result of applying these criteria to the first PCA principal component that has the highest variance.…”

Section: Resultsmentioning

confidence: 99%

“…In this research, the determination of the neural network topology to predict wind speed is based on previous research ( Ibnu Choldun, Santoso & Surendro, 2020 ; Rachmatullah, Santoso & Surendro, 2020 ) that uses PCA and clustering with the K-means technique as illustrated in Fig. 2 , but for the regression objective function.…”

Section: Methodsmentioning

confidence: 99%

“…In this research, the determination of the neural network topology use PCA and K-means clustering ( Rachmatullah, Santoso & Surendro, 2020 ), but for the new objective function. Whereas in the previous research ( Ibnu Choldun, Santoso & Surendro, 2020 ; Rachmatullah, Santoso & Surendro, 2020 ) the determination of the neural network topology was used for the classification objective function, in this research it was used for the regression objective function, specifically to predict wind speed. The scientific major contribution of this research is the use of a new method to determine the neural network topology using PCA and clustering for the regression objective function.…”

Section: Introductionmentioning

confidence: 99%

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Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction

Rachmatullah

Santoso

Surendro

2021

PeerJ Computer Science

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Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. Topology determination means finding the hidden layers number and the hidden neurons number for corresponding hidden layer in the neural network. The difference between this research and previous research is that the objective function of this research is regression, while the objective function of previous research is classification. Determination of the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used to predict wind speed. Then the performance of topology determination using PCA and clustering is then compared with several other methods. The results of the experiment show that the performance of the neural network topology determined using PCA and clustering has better performance than the other methods being compared. Performance is determined based on the RMSE value, the smaller the RMSE value, the better the neural network performance. In future research, it is necessary to apply a correlation or relationship between input attribute and output attribute and then analyzed, prior to conducting PCA and clustering analysis.

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Section: Resultssupporting

confidence: 80%

Section: Resultssupporting

confidence: 49%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%