Evaluation Of Feature Selection for Improvement Backpropagation Neural Network in Divorce Predictions

Simanjuntak, Manaris; Muljono, Muljono; Shidik, Guruh Fajar; Fanani, Ahmad Zainul

doi:10.1109/isemantic50169.2020.9234297

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Interestingly, with these same proportions, accuracy values between 95.59 and 98.53% were achieved for the remaining classifiers. Simanjuntak et al ( 2020 ) used the Reverse Propagation Neural Network (BPNN) algorithm. For a deeper evaluation, they contrasted the results by implementing various feature selection techniques (i.e., Information Gain, Gain Ratio, Relief-F and Correlation).…”

Section: Related Workmentioning

confidence: 99%

A distance-based kernel for classification via Support Vector Machines

Amaya-Tejera,

Gamarra,

Vélez

et al. 2024

Front. Artif. Intell.

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Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative approach where subsets of the original data are randomly selected to train the model multiple times. This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.

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Section: Related Workmentioning

confidence: 99%

A distance-based kernel for classification via Support Vector Machines

Amaya-Tejera,

Gamarra,

Vélez

et al. 2024

Front. Artif. Intell.

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show abstract

“…The different ranking methods and algorithms are described below. 1) Information Gain: -The gain information value is generated from the entropy value that has not been separated and then reduced by the entropy value of the results after separation [1].…”

Section: Introductionmentioning

confidence: 99%

“…The accuracy of the model is then calculated for each attribute, and the attribute with the highest accuracy is considered the most useful. [1] 3) Gain Ratio: -Gain Ratio (GR) is a modification of IG (Information Gain). IG is to form the induction of the decision tree (ID3), while the Gain Ratio is used in C4.5 is a transformation of ID3.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the Probability of Divorce Using Machine Learning Algorithms

Kumar¹,

Saha²,

Mondal³

et al. 2023

IJETMS

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For a happy and healthy life, a happy married life is very much essential. But nowadays, divorce cases are increasing rapidly day by day. According to a study, the divorce rate worldwide was 4.08% per 1000 married people in May 2022. For this reason, there is a need for effective prediction of divorce rate which helps the marriage counselor or therapist to understand how serious a case is. For this research purpose, a dataset was collected from UCI repository and contains some data based on the questions asked to the couple and the answers they gave. First, the dataset was cleaned using different ranking methods such as Information Gain, One R, Gain Ratio and ReliefF. Using these ranking methods, the most important fields that really affect the divorce are selected. Then, different classification algorithms such as Logistic Regression, Naïve Bayes, SGD, Decision Tree, Random Forest and Multilayer Perceptron were used and compared to find the accuracy. These algorithms are used first with all fields and then with 6 and 7 fields. These algorithms are used for 50:50, 66:34, 80:20 training/testing split and for 10-fold cross validation. When 7-fields are combined and checked with all algorithms for training-test and 10-fold cross validation, then 100% accuracy was found in Decision Tree, Random Forest and Multilayer Perceptron algorithms.

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Hybrid of Ensemble Machine Learning and Nature-Inspired Algorithms for Divorce Prediction

Sahle,

Yibre

2024

Communications in Computer and Information Science

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Evaluation Of Feature Selection for Improvement Backpropagation Neural Network in Divorce Predictions

Cited by 5 publications

References 6 publications

A distance-based kernel for classification via Support Vector Machines

A distance-based kernel for classification via Support Vector Machines

Prediction of the Probability of Divorce Using Machine Learning Algorithms

Hybrid of Ensemble Machine Learning and Nature-Inspired Algorithms for Divorce Prediction

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