Research Article
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Year 2023, Volume: 38 Issue: 3, 1919 - 1930, 06.01.2023
https://doi.org/10.17341/gazimmfd.1087746

Abstract

References

  • 1. Yazici, M. T., Basurra, S., & Gaber, M. M., Edge machine learning: Enabling smart internet of things applications. Big data and cognitive computing, 2 (3), 26, 2018.
  • 2. Merenda, M., Porcaro, C., & Iero, D., Edge machine learning for ai-enabled iot devices: A review. Sensors, 20 (9), 2533, 2020.
  • 3. Murshed, M. S., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., & Hussain, F., Machine learning at the network edge: A survey. ACM Computing Surveys (CSUR), 54 (8), 1-37, 2021.
  • 4. Du, M., Wang, K., Chen, Y., Wang, X., & Sun, Y., Big data privacy preserving in multi-access edge computing for heterogeneous Internet of Things. IEEE Communications Magazine, 56 (8), 62-67, 2018.
  • 5. Büyüknacar, Y., Canbay, Y., Federe öğrenme ve veri mahremiyeti, 2021.
  • 6. Priya, S., & Selvakumar, S., PaSOFuAC: Particle Swarm Optimization Based Fuzzy Associative Classifier for Detecting Phishing Websites. Wireless Personal Communications, 1-30, 2022.
  • 7. Çetin, E., & Ortalaş, F., Elektrikli ve Otonom Araçlarda Makine Öğrenmesi Kullanarak Trafik Levhaları Tanıma ve Simülasyon Uygulaması. El-Cezeri, 8 (3), 1081-1092, 2021.
  • 8. Gözüaçık, N., A Virtual Assistant for Predicting Defective Software Module, 29th Signal Processing and Communications Applications Conference (SIU), IEEE, 1-4, June, 2021.
  • 9. Gökdemir, A., Çalhan, A., Deep learning and machine learning based anomaly detection in internet of things environments, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (4), 1945-1956, 2022.
  • 10. Güngör, E., Sinem, A. K., & Orman, Z., Makine Öğrenmesine Dayalı Mobil İngilizce Öğrenme Uygulaması, Bilgisayar Bilimleri ve Teknolojileri Dergisi, 1 (2), 58-65, 2021.
  • 11. Liu, B., Hsu, W., & Ma, Y., Integrating classification and association rule mining, In Kdd, Vol. 98, pp. 80-86, August, 1998.
  • 12. [12] Li, W., Han, J., & Pei, J., CMAR: Accurate and efficient classification based on multiple class-association rules, In Proceedings 2001 IEEE international conference on data mining, 369-376, November, 2001.
  • 13. Thabtah, F., Cowling, P., & Peng, Y., MCAR: multi-class classification based on association rule, In The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 33, January, 2005.
  • 14. Alwidian, J., Hammo, B., & Obeid, N., Enhanced CBA algorithm based on apriori optimization and statistical ranking measure, In Proceeding of 28th International Business Information Management Association (IBIMA) conference on Vision, 2020, 4291-4306, 2016.
  • 15. Alwidian, J., Hammo, B., & Obeid, N., FCBA: fast classification based on association rules algorithm, International Journal of Computer Science and Network Security (IJCSNS), 16 (12), 117, 2016.
  • 16. Alwidian, J., Hammo, B. H., & Obeid, N., WCBA: Weighted classification based on association rules algorithm for breast cancer disease, Applied Soft Computing, 62, 536-549, 2018.
  • 17. Gepperth, A., & Hammer, B., Incremental learning algorithms and applications, In European symposium on artificial neural networks (ESANN), 2016.
  • 18. Hu, C., Chen, Y., Hu, L., & Peng, X., A novel random forests based class incremental learning method for activity recognition, Pattern Recognition, 78, 277-290, 2018.
  • 19. Tanarat, S., & Kreesuradej, W., Incremental Classification Based on Association Rules Algorithm (ICBA), In Proceedings of the International Conference on Data Science (ICDATA), p. 1, The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2011.
  • 20. Alnababteh, M. H., Alfyoumi, M., Aljumah, A., & Ababneh, J., Associative Classification Based on Incremental Mining (ACIM), International Journal of Computer Theory and Engineering, 6 (2), 135, 2014.
  • 21. Al-Fayoumi, M. A., Enhanced Associative classification based on incremental mining Algorithm (E-ACIM), International Journal of Computer Science Issues (IJCSI), 12 (1), 124, 2015.
  • 22. Tang, C., Li, W., Wang, P., & Wang, L., Online human action recognition based on incremental learning of weighted covariance descriptors, Information Sciences, 467, 219-237, 2018.
  • 23. Ristin, M., Guillaumin, M., Gall, J., & Van Gool, L., Incremental learning of random forests for large-scale image classification, IEEE transactions on pattern analysis and machine intelligence, 38 (3), 490-503, 2015.
  • 24. Wu, C. J., Brooks, D., Chen, K., Chen, D., Choudhury, S., Dukhan, M., ... & Zhang, P., Machine learning at facebook: Understanding inference at the edge, In 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), 331-344, February, 2019.
  • 25. Jiang, J.C., et al., Federated learning in smart city sensing: Challenges and opportunities.Sensors, 20 (21), 6230, 2020.
  • 26. Liu, Y., et al., A systematic literature review on federated learning: From a model quality perspective. arXiv preprint arXiv:2012.01973, 2020.
  • 27. Yang, Q., et al., Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 13 (3), 1-207, 2019.
  • 28. Browne, P. R., Sweeting, A. J., & Robertson, S., Modelling the Influence of Task Constraints on Goal Kicking Performance in Australian Rules Football, Sports Medicine-Open, 8 (1), 1-12, 2022.
  • 29. Agrawal, R., Imieliński, T., & Swami, A., Mining association rules between sets of items in large databases, In Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 207-216, June, 1993.
  • 30. Menzies, T., & Hu, Y., Data mining for very busy people, Computer, 36 (11), 22-29, 2003.
  • 31. pyarc 1.1.4. https://pypi.org/project/pyarc/. Yayın tarihi Aralık 9, 2020. Erişim tarihi Aralık 26, 2021.
  • 32. Agrawal, R., & Srikant, R., Fast algorithms for mining association rules, In Proc. 20th int. conf. very large data bases, VLDB, 1215, 487-499, September, 1994.
  • 33. Abdelhamid, N., Multi-label rules for phishing classification, Applied Computing and Informatics, 11 (1), 29-46, 2015.
  • 34. Moh'd Iqbal, A. L., Hadi, W. E., & Alwedyan, J., Detecting Phishing Websites Using Associative Classification, Journal of Information Engineering and Applications, 3, 2013.
  • 35. Thabtah, F., Hadi, W., Abdelhamid, N., & Issa, A., Prediction phase in associative classification mining, International Journal of Software Engineering and Knowledge Engineering, 21 (06), 855-876, 2011.
  • 36. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/car+evaluation. Erişim tarihi Aralık 26, 2021.
  • 37. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/bank+marketing. Erişim tarihi Aralık 26, 2021.
  • 38. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/mushroom. Erişim tarihi Aralık 26, 2021.
  • 39. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/nursery. Erişim tarihi Aralık 26, 2021.
  • 40. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/adult. Erişim tarihi Aralık 26, 2021.

du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisi

Year 2023, Volume: 38 Issue: 3, 1919 - 1930, 06.01.2023
https://doi.org/10.17341/gazimmfd.1087746

Abstract

İstemci sunucu sistemlerinde makine öğrenmesi modeli kullanılması bir ihtiyaçtır. Ancak istemcilerden verilerin toplanması, sunucuya aktarılması, makine öğrenmesi modeli eğitilmesi ve bu modelin istemcilerde çalışan cihazlara entegre edilmesi bir çok problemi beraberinde getirmektedir. Verilerin istemcilerden sunucuya transferi ağ trafiğine sebep olmakta, fazla enerji gerektirmekte ve veri mahremiyetini istismar edilebilmektedir. Çalışma kapsamında, bahsedilen problemlere çözüm için federe öğrenme mimarisi kullanılmaktadır. Mimariye göre, her bir istemcide istemcinin kendi verilerinden makine öğrenmesi modeli eğitilmektedir. Her bir istemcide eğitilen modeller sunucuya gönderilmekte ve sunucuda bu modeller birleştirilerek yeni bir model oluşturulmaktadır. Oluşturulan nihai model tekrar istemcilere dağıtılmaktadır. Bu çalışmada Veriden Habersiz İlişkili Kurallara Dayalı Sınıflandırma (Data Unaware Classification Based on Association, du-CBA) olarak adlandırılan ilişkisel sınıflandırma algoritması geliştirilmiştir. Federe öğrenme ile klasik öğrenme mimarilerini karşılaştırıp başarılarını ölçmek için çalışma kapsamında benzetim ortamı oluşturulmuştur. Benzetim ortamında du-CBA ve CBA algoritmaları kullanılarak modeller eğitilmiş ve sonuçlar kıyaslanmıştır. Modellerin eğitiminde University of California Irvine (UCI) veri havuzundan alınan beş veri seti kullanılmıştır. Deneysel sonuçlar, her bir veri seti için federe öğrenme ile eğitilen modellerin, klasik öğrenme ile eğitilen modellerle neredeyse aynı doğruluğu elde ettiğini ama eğitim sürelerinin yaklaşık %70 oranında azaldığını göstermiştir. Sonuçlar geliştirilen algoritmanın başarıya ulaştığını ortaya koymaktadır.

References

  • 1. Yazici, M. T., Basurra, S., & Gaber, M. M., Edge machine learning: Enabling smart internet of things applications. Big data and cognitive computing, 2 (3), 26, 2018.
  • 2. Merenda, M., Porcaro, C., & Iero, D., Edge machine learning for ai-enabled iot devices: A review. Sensors, 20 (9), 2533, 2020.
  • 3. Murshed, M. S., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., & Hussain, F., Machine learning at the network edge: A survey. ACM Computing Surveys (CSUR), 54 (8), 1-37, 2021.
  • 4. Du, M., Wang, K., Chen, Y., Wang, X., & Sun, Y., Big data privacy preserving in multi-access edge computing for heterogeneous Internet of Things. IEEE Communications Magazine, 56 (8), 62-67, 2018.
  • 5. Büyüknacar, Y., Canbay, Y., Federe öğrenme ve veri mahremiyeti, 2021.
  • 6. Priya, S., & Selvakumar, S., PaSOFuAC: Particle Swarm Optimization Based Fuzzy Associative Classifier for Detecting Phishing Websites. Wireless Personal Communications, 1-30, 2022.
  • 7. Çetin, E., & Ortalaş, F., Elektrikli ve Otonom Araçlarda Makine Öğrenmesi Kullanarak Trafik Levhaları Tanıma ve Simülasyon Uygulaması. El-Cezeri, 8 (3), 1081-1092, 2021.
  • 8. Gözüaçık, N., A Virtual Assistant for Predicting Defective Software Module, 29th Signal Processing and Communications Applications Conference (SIU), IEEE, 1-4, June, 2021.
  • 9. Gökdemir, A., Çalhan, A., Deep learning and machine learning based anomaly detection in internet of things environments, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (4), 1945-1956, 2022.
  • 10. Güngör, E., Sinem, A. K., & Orman, Z., Makine Öğrenmesine Dayalı Mobil İngilizce Öğrenme Uygulaması, Bilgisayar Bilimleri ve Teknolojileri Dergisi, 1 (2), 58-65, 2021.
  • 11. Liu, B., Hsu, W., & Ma, Y., Integrating classification and association rule mining, In Kdd, Vol. 98, pp. 80-86, August, 1998.
  • 12. [12] Li, W., Han, J., & Pei, J., CMAR: Accurate and efficient classification based on multiple class-association rules, In Proceedings 2001 IEEE international conference on data mining, 369-376, November, 2001.
  • 13. Thabtah, F., Cowling, P., & Peng, Y., MCAR: multi-class classification based on association rule, In The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 33, January, 2005.
  • 14. Alwidian, J., Hammo, B., & Obeid, N., Enhanced CBA algorithm based on apriori optimization and statistical ranking measure, In Proceeding of 28th International Business Information Management Association (IBIMA) conference on Vision, 2020, 4291-4306, 2016.
  • 15. Alwidian, J., Hammo, B., & Obeid, N., FCBA: fast classification based on association rules algorithm, International Journal of Computer Science and Network Security (IJCSNS), 16 (12), 117, 2016.
  • 16. Alwidian, J., Hammo, B. H., & Obeid, N., WCBA: Weighted classification based on association rules algorithm for breast cancer disease, Applied Soft Computing, 62, 536-549, 2018.
  • 17. Gepperth, A., & Hammer, B., Incremental learning algorithms and applications, In European symposium on artificial neural networks (ESANN), 2016.
  • 18. Hu, C., Chen, Y., Hu, L., & Peng, X., A novel random forests based class incremental learning method for activity recognition, Pattern Recognition, 78, 277-290, 2018.
  • 19. Tanarat, S., & Kreesuradej, W., Incremental Classification Based on Association Rules Algorithm (ICBA), In Proceedings of the International Conference on Data Science (ICDATA), p. 1, The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2011.
  • 20. Alnababteh, M. H., Alfyoumi, M., Aljumah, A., & Ababneh, J., Associative Classification Based on Incremental Mining (ACIM), International Journal of Computer Theory and Engineering, 6 (2), 135, 2014.
  • 21. Al-Fayoumi, M. A., Enhanced Associative classification based on incremental mining Algorithm (E-ACIM), International Journal of Computer Science Issues (IJCSI), 12 (1), 124, 2015.
  • 22. Tang, C., Li, W., Wang, P., & Wang, L., Online human action recognition based on incremental learning of weighted covariance descriptors, Information Sciences, 467, 219-237, 2018.
  • 23. Ristin, M., Guillaumin, M., Gall, J., & Van Gool, L., Incremental learning of random forests for large-scale image classification, IEEE transactions on pattern analysis and machine intelligence, 38 (3), 490-503, 2015.
  • 24. Wu, C. J., Brooks, D., Chen, K., Chen, D., Choudhury, S., Dukhan, M., ... & Zhang, P., Machine learning at facebook: Understanding inference at the edge, In 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), 331-344, February, 2019.
  • 25. Jiang, J.C., et al., Federated learning in smart city sensing: Challenges and opportunities.Sensors, 20 (21), 6230, 2020.
  • 26. Liu, Y., et al., A systematic literature review on federated learning: From a model quality perspective. arXiv preprint arXiv:2012.01973, 2020.
  • 27. Yang, Q., et al., Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 13 (3), 1-207, 2019.
  • 28. Browne, P. R., Sweeting, A. J., & Robertson, S., Modelling the Influence of Task Constraints on Goal Kicking Performance in Australian Rules Football, Sports Medicine-Open, 8 (1), 1-12, 2022.
  • 29. Agrawal, R., Imieliński, T., & Swami, A., Mining association rules between sets of items in large databases, In Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 207-216, June, 1993.
  • 30. Menzies, T., & Hu, Y., Data mining for very busy people, Computer, 36 (11), 22-29, 2003.
  • 31. pyarc 1.1.4. https://pypi.org/project/pyarc/. Yayın tarihi Aralık 9, 2020. Erişim tarihi Aralık 26, 2021.
  • 32. Agrawal, R., & Srikant, R., Fast algorithms for mining association rules, In Proc. 20th int. conf. very large data bases, VLDB, 1215, 487-499, September, 1994.
  • 33. Abdelhamid, N., Multi-label rules for phishing classification, Applied Computing and Informatics, 11 (1), 29-46, 2015.
  • 34. Moh'd Iqbal, A. L., Hadi, W. E., & Alwedyan, J., Detecting Phishing Websites Using Associative Classification, Journal of Information Engineering and Applications, 3, 2013.
  • 35. Thabtah, F., Hadi, W., Abdelhamid, N., & Issa, A., Prediction phase in associative classification mining, International Journal of Software Engineering and Knowledge Engineering, 21 (06), 855-876, 2011.
  • 36. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/car+evaluation. Erişim tarihi Aralık 26, 2021.
  • 37. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/bank+marketing. Erişim tarihi Aralık 26, 2021.
  • 38. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/mushroom. Erişim tarihi Aralık 26, 2021.
  • 39. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/nursery. Erişim tarihi Aralık 26, 2021.
  • 40. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/adult. Erişim tarihi Aralık 26, 2021.
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Büşra Büyüktanır 0000-0003-2571-4029

Kazım Yıldız 0000-0001-6999-1410

Eyüp Emre Ülkü 0000-0002-1985-6461

Tolga Büyüktanır 0000-0001-5317-0028

Publication Date January 6, 2023
Submission Date March 14, 2022
Acceptance Date September 17, 2022
Published in Issue Year 2023 Volume: 38 Issue: 3

Cite

APA Büyüktanır, B., Yıldız, K., Ülkü, E. E., Büyüktanır, T. (2023). du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 38(3), 1919-1930. https://doi.org/10.17341/gazimmfd.1087746
AMA Büyüktanır B, Yıldız K, Ülkü EE, Büyüktanır T. du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisi. GUMMFD. January 2023;38(3):1919-1930. doi:10.17341/gazimmfd.1087746
Chicago Büyüktanır, Büşra, Kazım Yıldız, Eyüp Emre Ülkü, and Tolga Büyüktanır. “Du-CBA: Veriden Habersiz Ve artırımlı sınıflandırmaya Dayalı Birliktelik Kuralları çıkarma Mimarisi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 38, no. 3 (January 2023): 1919-30. https://doi.org/10.17341/gazimmfd.1087746.
EndNote Büyüktanır B, Yıldız K, Ülkü EE, Büyüktanır T (January 1, 2023) du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 38 3 1919–1930.
IEEE B. Büyüktanır, K. Yıldız, E. E. Ülkü, and T. Büyüktanır, “du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisi”, GUMMFD, vol. 38, no. 3, pp. 1919–1930, 2023, doi: 10.17341/gazimmfd.1087746.
ISNAD Büyüktanır, Büşra et al. “Du-CBA: Veriden Habersiz Ve artırımlı sınıflandırmaya Dayalı Birliktelik Kuralları çıkarma Mimarisi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 38/3 (January 2023), 1919-1930. https://doi.org/10.17341/gazimmfd.1087746.
JAMA Büyüktanır B, Yıldız K, Ülkü EE, Büyüktanır T. du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisi. GUMMFD. 2023;38:1919–1930.
MLA Büyüktanır, Büşra et al. “Du-CBA: Veriden Habersiz Ve artırımlı sınıflandırmaya Dayalı Birliktelik Kuralları çıkarma Mimarisi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 38, no. 3, 2023, pp. 1919-30, doi:10.17341/gazimmfd.1087746.
Vancouver Büyüktanır B, Yıldız K, Ülkü EE, Büyüktanır T. du-CBA: Veriden habersiz ve artırımlı sınıflandırmaya dayalı birliktelik kuralları çıkarma mimarisi. GUMMFD. 2023;38(3):1919-30.