Synthetic Data Generation with Modified Artificial Bee Colony Optimization Algorithm and Statistical Modeling

Fatma Akalın

Makale detayları

Synthetic Data Generation with Modified Artificial Bee Colony Optimization Algorithm and Statistical Modeling

Yazarlar:

Fatma Akalın

Yayıncı:

Igdir University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 4, pp.1408-1431

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331741

pISSN:

2536-4618

Anahtar kelimeler:

Özet:

 Machine learning is a powerful decision support system used in analyzing and evaluating real-life data. This system aims to create new solutions and improve performance. Therefore, it is related to the field of data science. There are data on the basis of this relationship The effectiveness of drawing meaningful insights from data depends on the quality of the models training. To improve this performance, the variety of combinations among the data and the total number of data in the dataset should be increased. But in this topic, insufficient data access, legal regulations, ethical rules, confidentiality procedures, privacy, data sharing restrictions and cost parameters are obstacles. Synthetic data generation is a basic step in the field of data science in order to solve all these problems, improve functionality and provide powerful machine-learning inferences. Therefore, a new synthetic data generation approach consisting of 3 basic stages is proposed in this study. In the first stage, synthetic data production similar to the distribution of the original data was carried out with the modified ABC Artificial Bee Colony optimization algorithm. In the second stage, the category information of the independent variables was determined by the statistical evaluation analyzed with regression methods among the artificial data produced. In the third stage, the efficiency and applicability of the artificial data produced were evaluated with supervised machine learning classifiers. As a result of the evaluation, it has been proven that the proposed synthetic data generation approach improves the performance of machine learning classifiers in proportion to the increasing number of data. The decision tree algorithm that showed maximum performance produced success rates of 100, 92.5, 100, 85, and 66 on 5 separate enriched datasets, respectively.

 Machine learning is a powerful decision support system used in analyzing and evaluating real-life data. This system aims to create new solutions and improve performance. Therefore, it is related to the field of data science. There are data on the basis of this relationship The effectiveness of drawing meaningful insights from data depends on the quality of the models training. To improve this performance, the variety of combinations among the data and the total number of data in the dataset should be increased. But in this topic, insufficient data access, legal regulations, ethical rules, confidentiality procedures, privacy, data sharing restrictions and cost parameters are obstacles. Synthetic data generation is a basic step in the field of data science in order to solve all these problems, improve functionality and provide powerful machine-learning inferences. Therefore, a new synthetic data generation approach consisting of 3 basic stages is proposed in this study. In the first stage, synthetic data production similar to the distribution of the original data was carried out with the modified ABC Artificial Bee Colony optimization algorithm. In the second stage, the category information of the independent variables was determined by the statistical evaluation analyzed with regression methods among the artificial data produced. In the third stage, the efficiency and applicability of the artificial data produced were evaluated with supervised machine learning classifiers. As a result of the evaluation, it has been proven that the proposed synthetic data generation approach improves the performance of machine learning classifiers in proportion to the increasing number of data. The decision tree algorithm that showed maximum performance produced success rates of 100, 92.5, 100, 85, and 66 on 5 separate enriched datasets, respectively.

 Machine learning is a powerful decision support system used in analyzing and evaluating real-life data. This system aims to create new solutions and improve performance. Therefore, it is related to the field of data science. There are data on the basis of this relationship The effectiveness of drawing meaningful insights from data depends on the quality of the models training. To improve this performance, the variety of combinations among the data and the total number of data in the dataset should be increased. But in this topic, insufficient data access, legal regulations, ethical rules, confidentiality procedures, privacy, data sharing restrictions and cost parameters are obstacles. Synthetic data generation is a basic step in the field of data science in order to solve all these problems, improve functionality and provide powerful machine-learning inferences. Therefore, a new synthetic data generation approach consisting of 3 basic stages is proposed in this study. In the first stage, synthetic data production similar to the distribution of the original data was carried out with the modified ABC Artificial Bee Colony optimization algorithm. In the second stage, the category information of the independent variables was determined by the statistical evaluation analyzed with regression methods among the artificial data produced. In the third stage, the efficiency and applicability of the artificial data produced were evaluated with supervised machine learning classifiers. As a result of the evaluation, it has been proven that the proposed synthetic data generation approach improves the performance of machine learning classifiers in proportion to the increasing number of data. The decision tree algorithm that showed maximum performance produced success rates of 100, 92.5, 100, 85, and 66 on 5 separate enriched datasets, respectively.

 Makine öğrenmesi, gerçek yaşam verilerini analiz etmede ve değerlendirmede kullanılan güçlü bir karar destek sistemidir. Bu sistem, yeni çözümler üretmeyi ve performansı iyileştirmeyi amaçlamaktadır. Bu nedenle, veri bilimi alanıyla ilişkilidir. Bu ilişki temelinde veriler vardır. Verilerden anlamlı içgörüler çıkarma etkinliği, model eğitiminin kalitesine bağlıdır. Bu performansı iyileştirmek için, veriler arasındaki kombinasyonların çeşitliliği ve veri kümesindeki toplam veri sayısı artırılmalıdır. Ancak bu konuda, yetersiz veri erişimi, yasal düzenlemeler, etik kurallar, gizlilik prosedürleri, gizlilik, veri paylaşımı kısıtlamaları ve maliyet parametreleri engellerdir. Tüm bu sorunları çözmek, işlevselliği iyileştirmek ve güçlü makine öğrenimi çıkarımları sağlamak için sentetik veri üretimi, veri bilimi alanında temel bir adımdır. Bu nedenle, bu çalışmada 3 temel aşamadan oluşan yeni bir sentetik veri üretimi yaklaşımı önerilmiştir. İlk aşamada, orijinal verilerin dağılımına benzer şekilde sentetik veri üretimi, modifiye edilmiş ABC Yapay Arı Kolonisi optimizasyon algoritması ile gerçekleştirilmiştir. İkinci aşamada, üretilen yapay veriler arasında regresyon yöntemleriyle analiz edilen istatistiksel değerlendirme ile bağımsız değişkenlerin kategori bilgileri belirlenmiştir. Üçüncü aşamada, üretilen yapay verilerin etkinliği ve uygulanabilirliği, makine öğrenimi sınıflandırıcıları ile değerlendirilmiştir. Değerlendirme sonucunda, önerilen sentetik veri üretim yönteminin, veri sayısının artışı ile orantılı olarak makine öğrenmesi sınıflandırıcılarının performansını artırdığı kanıtlanmıştır Maksimum performans gösteren karar ağacı algoritması, zenginleştirilmiş 5 ayrı veri kümesi üzerinde sırasıyla 100, 92.5, 100, 85, 66 başarı oranları üretmiştir.

Makale detayları

Synthetic Data Generation with Modified Artificial Bee Colony Optimization Algorithm and Statistical Modeling

Yazarlar:

Fatma Akalın

Yayıncı:

Igdir University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 4, pp.1408-1431

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331741

pISSN:

2536-4618

Synthetic data generation

Modified ABC optimization algorithm

R-squared coefficient

Polynomial regression

Decision tree classifierSentetik veri üretimi Değiştirilmiş ABC optimizasyon algoritması

R-kare katsayısı

Polinom regresyonu

Karar ağacı sınıflandırıcısı

Sayı

Makale detayları

Yazarlar: Fatma Akalın

Yayıncı: Igdir University

Ana Başlıklar: Yazılım Mühendisliği (Diğer)

Dergi: Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 4, pp.1408-1431

Belge Tipi: Makale

Yayın Tarihi: 1/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001331741

pISSN: 2536-4618

Sayı

Yazarlar:

Fatma Akalın

Yayıncı:

Igdir University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.14 Issue 4, pp.1408-1431

Belge Tipi:

Makale

Yayın Tarihi:

1/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001331741

pISSN:

2536-4618