A proposed method for handling an imbalance data in classification of blood type based on Myers-Briggs type indicator

Akbar, Ahmad Taufiq; Husaini, Rochmat; Akbar, Bagus Muhammad; Saifullah, Shoffan

doi:10.14710/jtsiskom.2020.13625

Cited by 8 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ini menunjukkan pada beberapa kondisi, seleksi fitur kurang signifikan dalam memperbaiki akurasi klasifikasi dibanding dengan modifikasi data melalui resampling untuk menyeimbangkan data training sehingga performa klasifikasi meningkat. Penelitian oleh Akbar et al juga mengenai klasifikasi pima diabetes menggunakan preprocessing supervised resample dan unsupervised resample, kemudian diklasifikasikan dengan k-NN menghasilkan akurasi mencapai 92,8% melalui 10 fold x validation [13].…”

Section: Pendahuluanunclassified

Preprocessing Using SMOTE and K-Means for Classification by Logistic Regression on Pima Indian Diabetes Dataset

Akbar,

Husaini,

Prapcoyo

2023

Telematika

View full text Add to dashboard Cite

Purpose: Our study aims to combine pre-processing methods to develop a training data model from the Indian diabetic Pima dataset so that it can improve the performance of machine learning in recognizing diabetesDesign/methodology/approach: This research was started through several stages such as collecting the Pima indian diabetes dataset, pre-processing including k-means clustering, oversampling using SMOTE, then undersampling the dataset whose cluster is a minority in each class. Furthermore, the dataset is classified using machine learning namely logistic regression through 10 cross validationFindings/result: The results of this classification performance show that the accuracy reaches 99.5% and is higher than the method in previous studies.Originality/value/state of the art:The method in this study uses SMOTE to handle data imbalances and k-means clustering to remove outliers by removing labels that do not match the majority cluster in each class so that clean data is produced and validation using logistic regression is more accurate than previous studies.Tujuan: Penelitian ini bertujuan untuk menerapkan metode pre-processing untuk membentuk model data latih dari dataset Pima Indian diabetes sehingga dapat meningkatkan performa mesin pembelajaran dalam mengenali diabetes.Perancangan/metode/pendekatan: Riset ini dimulai melalui beberapa tahap yakni pengumpulan dataset Pima Indian diabetes, pre-processing meliputi clustering, oversampling menggunakan SMOTE, kemudian undersampling pada dataset pada klaster minoritas pada setiap kelas. Selanjutnya dataset diklasifikasikan menggunakan machine learning yakni metode regresi logistik melalui 10 cross validationHasil: Hasil dari performa klasifikasi ini menunjukkan akurasi mencapai 99,5% dan lebih tinggi daripada metode pada penelitian sebelumnya.Keaslian/ state of the art: Metode dalam penelitian ini menggunakan SMOTE untuk menangani ketidakseimbangan data dan k-means klastering untuk membuang outlier dengan cara menghapus label yang tidak sesuai dengan klaster mayoritas pada setiap kelas sehingga dihasilkan data yang bersih dan pada validasi menggunakan logistic regression lebih akurat daripada penelitian sebelumnya.

show abstract

Section: Pendahuluanunclassified

Preprocessing Using SMOTE and K-Means for Classification by Logistic Regression on Pima Indian Diabetes Dataset

Akbar,

Husaini,

Prapcoyo

2023

Telematika

View full text Add to dashboard Cite

show abstract

“…Data balancing is carried out at this stage using the Synthetic Minority Oversampling Technique (SMOTE) method [16]. SMOTE is one of the most popular methods for dealing with data distribution imbalances [17].…”

Section: A Preprocessing Datamentioning

confidence: 99%

Data scaling performance on various machine learning algorithms to identify abalone sex

Arifin

Ariawan

Rosalia

et al. 2021

J. Teknol. dan Sist. Komput

View full text Add to dashboard Cite

This study aims to analyze the performance of machine learning algorithms with the data scaling process to show the method's effectiveness. It uses min-max (normalization) and zero-mean (standardization) data scaling techniques in the abalone dataset. The stages carried out in this study included data normalization on the data of abalone physical measurement features. The model evaluation was carried out using k-fold cross-validation with the number of k-fold 10. Abalone datasets were normalized in machine learning algorithms: Random Forest, Naïve Bayesian, Decision Tree, and SVM (RBF kernels and linear kernels). The eight features of the abalone dataset show that machine learning algorithms did not too influence data scaling. There is an increase in the performance of SVM, while Random Forest decreases when the abalone dataset is applied to data scaling. Random Forest has the highest average balanced accuracy (74.87%) without data scaling.

show abstract

“…Pada tahap ini dilakukan penyeimbangan data dengan menggunakan algoritme Synthetic Minority Oversampling Technique (SMOTE) (Akbar et al, 2020). SMOTE adalah salah satu algoritme atau metode yang paling populer digunakan untuk menangani ketidakseimbangan distribusi data (Kasanah at al 2019).…”

Section: Preprocessing Dataunclassified

Identifikasi Spesies Mangrove Menggunakan Algoritme Random Forest

Ariawan,

Rosalia,

Anzani

et al. 2021

Indonesian J.Maritime

View full text Add to dashboard Cite

Identification of mangrove species composition is an important topic in management and conservation of coastal ecosystem. The biodiversity of mangrove sp. affects the sustainability and balance of the entitiy-related in its ecosystem. This study aims to explore the potential of Machine Learning to identify mangrove sp. Specifically, the Random Forest algorithm is used to classify six mangrove species, that are: Avicennia eucalyptifolia, Bruguiera gymnorrhiza, Rhizophora apiculata, Rhizophora mucronata, Unrecorded Sp., and Xylocarpus granatum. Several approaches were taken to strengthen the performance of the Random Forest algorithm, that are preprocessing (SMOTE) and min-max normalization to balance the data distribution. The result shows that the projection of the normalization range (interval 0-1) has no effect in reducing the data pattern dimensionally. After preprocessing and normalization, five attributes (species, wood density, diameter at beast height, total of above ground biomass, and below-ground root) were classified and analyzed with species as the target attribute. The construction of model parameters is based on the total number of SMOTE results by specifying 100 and 500 as the number of single tree and 1000 as the number of nodes and the default predictor variable. The final result shows that the Random Forest algorithm obtained an optimal evaluation value with an average of 99.97% using the number of single tree and the predetermined cut-offs. The maximum accuracy of 100% is obtained from the number of single tree and cut-off with the following sizes: (1) 500 and 80:20; (2) 500 and 90:10; and (3) 100 and 80:20. These results indicate that it is very effective

show abstract

A proposed method for handling an imbalance data in classification of blood type based on Myers-Briggs type indicator

Cited by 8 publications

References 16 publications

Preprocessing Using SMOTE and K-Means for Classification by Logistic Regression on Pima Indian Diabetes Dataset

Preprocessing Using SMOTE and K-Means for Classification by Logistic Regression on Pima Indian Diabetes Dataset

Data scaling performance on various machine learning algorithms to identify abalone sex

Identifikasi Spesies Mangrove Menggunakan Algoritme Random Forest

Contact Info

Product

Resources

About