scholarly journals Perbandingan Metode Regresi Logistik dan Random Forest untuk Klasifikasi Data Imbalanced (Studi Kasus: Klasifikasi Rumah Tangga Miskin di Kabupaten Karangasem, Bali Tahun 2017)

2019 ◽  
Vol 16 (1) ◽  
pp. 58
Author(s):  
Taly Purwa
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Cross Validation ◽  
Under Sampling

Penelitian ini bertujuan untuk mendapatkan model terbaik untuk klasifikasi data imbalanced, yaitu  rumah tangga sampel Susenas Maret 2017 di Kabupaten Karangasem, ke dalam kategori miskin atau tidak. Metode yang digunakan adalah Regresi Logistik dan Random Forest dimana masing-masing diterapkan skema cross validation (CV), yaitu stratified 5-fold CV, skema under sampling, oversampling dan combine sampling untuk mengatasi masalah data imbalanced serta proses feature selection. Hasil penelitian menunjukkan bahwa penerapan skema under sampling, oversampling dan combine sampling pada model Regresi Logistik memberikan efek meningkatnya rata-rata nilai sensitivity dan turunnya rata-rata nilai akurasi dan specificity. Sedangkan pada model Random Forest, efek tersebut hanya terlihat dari hasil skema under sampling saja. Proses feature selection dapat menurunkan varian nilai akurasi, specificity, sensitivity dan AUC pada model Regresi Logistik dan Random Forest hanya pada skema tertentu. Model terbaik secara keseluruhan adalah model model Regresi Logistik dengan skema combine sampling dan tanpa proses feature selection dengan rata-rata nilai akurasi, specificity, sensitivity dan AUC masing-masing sebesar 78,13%, 79,16%, 64,44% dan 77,77%.

scholarly journals Phishing Website Detection Using Machine Learning Classifiers Optimized by Feature Selection

2020 ◽  
Vol 37 (4) ◽  
pp. 563-569
Author(s):  
Dželila Mehanović ◽  
Jasmin Kevrić
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Security Threats ◽  
Selection Methods ◽  
K Nearest Neighbor ◽  
Machine Learning Classifiers ◽  
Time To Build ◽  
Fold Cross Validation

Security is one of the most actual topics in the online world. Lists of security threats are constantly updated. One of those threats are phishing websites. In this work, we address the problem of phishing websites classification. Three classifiers were used: K-Nearest Neighbor, Decision Tree and Random Forest with the feature selection methods from Weka. Achieved accuracy was 100% and number of features was decreased to seven. Moreover, when we decreased the number of features, we decreased time to build models too. Time for Random Forest was decreased from the initial 2.88s and 3.05s for percentage split and 10-fold cross validation to 0.02s and 0.16s respectively.

scholarly journals Comparison of Random Forest, Logistic Regression, and MultilayerPerceptron Methods on Classification of Bank Customer Account Closure

2021 ◽  
Vol 4 (1) ◽  
pp. 14
Author(s):  
Husna Afanyn Khoirunissa ◽  
Amanda Rizky Widyaningrum ◽  
Annisa Priliya Ayu Maharani
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Feature Selection ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Cross Validation ◽  
Early Stage ◽  
Bank Account ◽  
Credit Score

<p>The Bank is a business entity that is dealing with money, accepting deposits from customers, providing funds for each withdrawal, billing checks on the customer's orders, giving credit and or embedding the excess deposits until required for repayment. The purpose of this research is to determine the influence of age, gender, country, customer credit score, number of bank products used by the customer, and the activation of the bank members in the decision to choose to continue using the bank account that he has retained or closed the bank account. The data in this research used 10,000 respondents originating from France, Spain, and Germany. The method used is data mining with early stage preprocessing to clean data from outlier and missing value and feature selection to select important attributes. Then perform the classification using three methods, which are Random Forest, Logistic Regression, and Multilayer Perceptron. The results of this research showed that the model with Multilayer Perceptron method with 10 folds Cross Validation is the best model with 85.5373% accuracy.</p><strong>Keywords:</strong> bank customer, random forest, logistic regression, multilayer perceptron

scholarly journals Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eiman Alothali ◽  
Kadhim Hayawi ◽  
Hany Alashwal
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Cross Validation ◽  
Performance Metrics ◽  
Area Under The Curve ◽  
Support Vector ◽  
Detection Systems ◽  
Vector Machines ◽  
Selection Approach ◽  
Feature Selection Approach

AbstractThe last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features: favorites_count, verified, statuses_count, and average_tweets_per_day, achieves good performance metrics for bot detection (84.1% precision, 81.2% recall).

scholarly journals An Improved Coronary Heart Disease Predictive System Using Random Forest

2021 ◽  
pp. 17-27
Author(s):  
Abdulraheem Abdul ◽  
Rafiu M. Isiaka ◽  
Ronke S. Babatunde ◽  
Jumoke F. Ajao
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor

Aims: This work aim is to develop an enhanced predictive system for Coronary Heart Disease (CHD). Study Design: Synthetic Minority Oversampling Technique and Random Forest. Methodology: The Framingham heart disease dataset was used, which was collected from a study in Framingham, Massachusetts, the data was cleaned, normalized, rebalanced. Classifiers such as random forest, artificial neural network, naïve bayes, logistic regression, k-nearest neighbor and support vector machine were used for classification. Results: Random Forest outperformed other classifiers with an accuracy of 98%, a sensitivity of 99% and a precision of 95.8%. Feature selection was employed for better classification, but  no significant improvement was recorded on the performance of the classifier with feature selection. Train test split also performed better that cross validation. Conclusion: Random Forest is recommended for research in Coronary Heart Disease prediction domain.

Prediction of Nitration Sites Based on FCBF Method and Stacking Ensemble Model

2021 ◽  
Vol 18 ◽  
Author(s):  
Min Liu ◽  
Lu Zhang ◽  
Xinyi Qin ◽  
Tao Huang ◽  
Ziwei Xu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Protein Sequence ◽  
Cross Validation ◽  
Transplant Rejection ◽  
Sampling Technique ◽  
Single Type ◽  
Post Translational Modification ◽  
Tyrosine Residues ◽  
Under Sampling ◽  
Fusion Features

Background: Nitration is one of the important Post-Translational Modification (PTM) occurring on the tyrosine residues of proteins. The occurrence of protein tyrosine nitration under disease conditions is inevitable and represents a shift from the signal transducing physiological actions of -NO to oxidative and potentially pathogenic pathways. Abnormal protein nitration modification can lead to serious human diseases, including neurodegenerative diseases, acute respiratory distress, organ transplant rejection and lung cancer. Objective: It is necessary and important to identify the nitration sites in protein sequences. Predicting that which tyrosine residues in the protein sequence are nitrated and which are not is of great significance for the study of nitration mechanism and related diseases. Methods: In this study, a prediction model of nitration sites based on the over-under sampling strategy and the FCBF method was proposed by stacking ensemble learning and fusing multiple features. Firstly, the protein sequence sample was encoded by 2701-dimensional fusion features (PseAAC, PSSM, AAIndex, CKSAAP, Disorder). Secondly, the ranked feature set was generated by the FCBF method according to the symmetric uncertainty metric. Thirdly, in the process of model training, use the over- and under- sampling technique was used to tackle the imbalanced dataset. Finally, the Incremental Feature Selection (IFS) method was adopted to extract an optimal classifier based on 10-fold cross-validation. Results and Conclusion: Results show that the model has significant performance advantages in indicators such as MCC, Recall and F1-score, no matter in what way the comparison was conducted with other classifiers on the independent test set, or made by cross-validation with single-type feature or with fusion-features on the training set. By integrating the FCBF feature ranking methods, over- and under- sampling technique and a stacking model composed of multiple base classifiers, an effective prediction model for nitration PTM sites was build, which can achieve a better recall rate when the ratio of positive and negative samples is highly imbalanced.

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