Modeling Treshold Liner in Transfer Function to Overcome Non Normality of the Errors

Kusdarwati, Heni; Handoyo, Samingun

doi:10.1088/1757-899x/546/5/052039

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Modeling the dengue fever status of a village [17] and modeling the baby's weight status at birth [18] are examples of classification modeling. Regression modeling generally aims to determine the magnitude of the influence of the predictor variable on the numerical response variable and also predicts an unknown value of the response variable based on the values of the predictor variables of a certain instance [19][20][21]. The research above only involves a single response variable and there has also been no effort to produce a model that is free from overfitting problems.…”

Section: Related Workmentioning

confidence: 99%

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Nugroho¹,

Handoyo²,

Hsieh³

et al. 2022

IJACSA

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The multiple regression model is very popular among researchers in both field of social and science because it is easy to interpret and have a well-established theoretical framework. However, the multioutput multiple regression model is actually widely applied in the engineering field because in the industrial world there are many systems with multiple outputs. The ridge regression model and the Multi-Layer Perceptron (MLP) neural network model are representations of the predictive linear regression model and predictive non-linear regression model that are widely applied in the world of practice. This study aims to build multi-output models of a ridge regression model and an MLP neural network whose hyperparameters are determined by a grid search algorithm through the cross-validation method. The hyperparameter that produces the smallest RMSE value in the validation data is chosen as the hyperparameter to train both models on the training data. The hyperparameter in question is a combination of learning algorithms and alpha values (ridge regression), a combination of the number of hidden nodes and gamma values (MLP neural network). In the ridge regression model for alpha in the range between 0.1 and 0.7, the smallest RMSE is obtained for all learning algorithms used. While the MLP neural network model specifically obtained a combination of the number of nodes = 18 and gamma = 0.1 which produces the smallest RMSE. The ridge regression model with selected hyperparameters has better performance (in the RMSE and R2 value) than the MLP neural network model with selected hyperparameters, both on training and testing data.

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Section: Related Workmentioning

confidence: 99%

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Nugroho¹,

Handoyo²,

Hsieh³

et al. 2022

IJACSA

Self Cite

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“…Some researchers had innovated a part of model developing elements in order to improve the performance of their developed model. The development of the secure Naïve Bayes classification model with optimal accuracy was done by Kjamilji et al [30], and the features weighted of the classification model input was carried out as an effort to improve model performance by Yue and Tang [31], while the threshold modification to overcome residuals normality was done by Kusdarwati and Handoyo [32]. The model performance measures also have an important role to select the best one.…”

Section: Related Workmentioning

confidence: 99%

Finding the Best Performance of Bayesian and Naïve Bayes Models in Fraudulent Firms Classification through Varying Threshold

Widodo¹,

Handoyo²,

Irianto³

et al. 2022

IJETAE

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—Fraud detection is the first step to preventing fraud committed by both individuals and organizations. The development of a high-performance classification model to detect fraud is an interesting topic in machine learning modeling. A finding of the best Bayesian and Naive Bayes classification models is a crucial issue because both models are simple and easily applied models in the fields of life and social sciences. This study aims to obtain the best performance of classification models developed based on probability concepts, namely Bayesian and Naive Bayes models. Adding a threshold value to the decision-making criteria of the two models is an effort expected to can find models that perform superiorly. Data on the auditing of fraudulent firms containing of 775 firms from various business sectors in Australia is used as a case study. The testing data consisting of 100 instances were taken by cluster random sampling with a proportion of 61 non-fraudulent and 39 fraudulent firms and the remaining instances as the training data. The best Bayesian model has an average accuracy of 84% obtained at a threshold value of 0.22. While the best Naive Bayes model has an average accuracy of 94% which is obtained in the 15 threshold values. Adding the threshold value has a significant impact on the performance of the Bayesian model, which can increase the average accuracy from 36% to 84%. On the other hand, the average accuracy of the Naive Bayes model only increased by 1%, from 93% to 94%. Performance measures Sensitivity, Specificity, F1 score, and ROC curve of the Naive Bayes model are also superior to the Bayesian model.

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“…The feature selection involving a structure model called the filter approach is not an easy task because the selection process has stages as many as the factorial of d features dimension [9]. Forward selection and backward elimination are popular filter methods applied in linear models both the regression [10] and classification models [11]. However, the application of the filter method in non-linear or assembled models such as a decision tree based on the concept of divide and conquer steps is not a precise decision choice [12].…”

Section: Introductionmentioning

confidence: 99%

Relevant Features Independence of Heuristic Selection and Important Features of Decision Tree in the Medical Data Classification

Mursityo,

Rupiwardani,

Putra

et al. 2024

JAIT

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The input of predictive models has an important role in directing the classification model to have a satisfactory performance in predicting an unknown label of the instance class. The predictor features should be not only relevant to the target feature but also should be independent of each other. The research objective is to obtain the predictor features that are relevant and independent through feature selection using the Chi-square test, one-way Analysis of Variance (ANOVA), and Pearson's correlation test, and to show important features of the decision tree are different from the features of relevant and independent. The evaluation of irrelevant features yields 44 of 67 relevant features which as many as 18 discrete types with two labels are dropped. The dataset with 44 relevant features is used to train the first decision tree. The relevant features mean that the target feature depends on them. The best predictor features should not only be relevant but also independent of each other. The evaluation of independent among features yields 11 of 44 independent features where the features of numeric and discrete with 2 labels are represented by 1 and three features respectively. The dataset with 11 relevant and independent features is used to train the second decision tree. The important features of both models are very different and the second model has better performance than the first one for the metrics of accuracy, recall, and F1-Score.

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Modeling Treshold Liner in Transfer Function to Overcome Non Normality of the Errors

Cited by 6 publications

References 3 publications

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Modeling Multioutput Response Uses Ridge Regression and MLP Neural Network with Tuning Hyperparameter through Cross Validation

Finding the Best Performance of Bayesian and Naïve Bayes Models in Fraudulent Firms Classification through Varying Threshold

Relevant Features Independence of Heuristic Selection and Important Features of Decision Tree in the Medical Data Classification

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